WORKING GROUP I CONTRIBUTION TO THE IPCC FIFTH ASSESSMENT REPORT
CLIMATE CHANGE 2013: THE PHYSICAL SCIENCE BASIS
Final Draft Underlying Scientific-Technical Assessment
A report accepted by Working Group I of the IPCC but not approved in detail.
Note:
The final draft Report, dated 7 June 2013, of the Working Group I contribution to the IPCC 5th Assessment
Report "Climate Change 2013: The Physical Science Basis" was accepted but not approved in detail by the
12th Session of Working Group I and the 36th Session of the IPCC on 26 September 2013 in Stockholm,
Sweden. It consists of the full scientific and technical assessment undertaken by Working Group I.
The Report has to be read in conjunction with the document entitled “Climate Change 2013: The Physical Science Basis.
Working Group I Contribution to the IPCC 5th Assessment Report - Changes to the underlying Scientific/Technical
Assessment” to ensure consistency with the approved Summary for Policymakers (IPCC-XXVI/Doc.4) and presented to the
Panel at its 36th Session. This document lists the changes necessary to ensure consistency between the full Report and the
Summary for Policymakers, which was approved line-by-line by Working Group I and accepted by the Panel at the abovementioned Sessions.
Before publication the Report will undergo final copyediting as well as any error correction as necessary, consistent with the
IPCC Protocol for Addressing Possible Errors. Publication of the Report is foreseen in January 2014.
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30 September 2013
WORKING GROUP I – TWELFTH SESSION
Stockholm, 23-26 September 2013
th
WG-I: 12 /Doc. 2b, CH02
(12.VIII.2013)
Agenda Item: 5
ENGLISH ONLY
WORKING GROUP I CONTRIBUTION TO THE IPCC FIFTH ASSESSMENT
REPORT (AR5), CLIMATE CHANGE 2013: THE PHYSICAL SCIENCE BASIS
Chapter 2: Observations: Atmosphere and Surface - Final Draft Underlying ScientificTechnical Assessment
(Submitted by the Co-Chairs of Working Group I)
Confidential – This document is being made available in preparation of
WGI-12 only and should not be cited, quoted, or distributed
NOTE:
The Final Draft Underlying Scientific-Technical Assessment is submitted to the Twelfth Session of Working
Group I for acceptance. The IPCC at its Thirty-sixth Session (Stockholm, 26 September 2013) will be informed
of the actions of the Twelfth Session of Working Group I in this regard.
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Final Draft (7 June 2013)
Chapter 2
IPCC WGI Fifth Assessment Report
Chapter 2: Observations: Atmosphere and Surface
Coordinating Lead Authors: Dennis L. Hartmann (USA), Albert M.G. Klein Tank (Netherlands), Matilde
Rusticucci (Argentina)
Lead Authors: Lisa Alexander (Australia), Stefan Broennimann (Switzerland), Yassine Abdul-Rahman
Charabi (Oman), Frank Dentener (EU/Netherlands), Ed Dlugokencky (USA), David Easterling (USA),
Alexey Kaplan (USA), Brian Soden (USA), Peter Thorne (USA/Norway/UK), Martin Wild (Switzerland),
Panmao Zhai (China)
Contributing Authors: Robert Adler (USA), Richard Allan (UK), Robert Allan (UK), Donald Blake
(USA), Owen Cooper (USA), Aiguo Dai (USA), Robert Davis (USA), Sean Davis (USA), Markus Donat
(Australia), Vitali Fioletov (Canada), Erich Fischer (Switzerland), Leopold Haimberger (Austria), Ben Ho
(USA), John Kennedy (UK), Elizabeth Kent (UK), Stefan Kinne (Germany), James Kossin (USA), Norman
Loeb (USA), Cathrine Lund Myhre (Norway), Carl Mears (USA), Christopher Merchant (UK), Steve
Montzka (USA), Colin Morice (UK), Joel Norris (USA), David Parker (UK), Bill Randel (USA), Andreas
Richter (Germany), Matthew Rigby (UK), Ben Santer (USA), Dian Seidel (USA), Tom Smith (USA), David
Stephenson (UK), Ryan Teuling (Netherlands), Junhong Wang (USA), Xiaolan Wang (Canada), Ray Weiss
(USA), Kate Willett (UK), Simon Wood (UK)
Review Editors: Jim Hurrell (USA), Jose Marengo (Brazil), Fredolin Tangang (Malaysia), Pedro Viterbo
(Portugal)
Date of Draft: 7 June 2013
Table of Contents
Executive Summary......................................................................................................................................... 3
2.1 Introduction ............................................................................................................................................. 7
Box 2.1: Uncertainty in Observational Records ........................................................................................... 8
2.2 Changes in Atmospheric Composition .................................................................................................. 9
2.2.1 Well-Mixed Greenhouse Gases ...................................................................................................... 9
2.2.2 Near Term Climate Forcers ......................................................................................................... 13
2.2.3 Aerosols ....................................................................................................................................... 18
Box 2.2: Quantifying Changes in the Mean: Trend Models and Estimation ........................................... 23
2.3 Changes in Radiation Budgets ............................................................................................................. 24
2.3.1 Global Mean Radiation Budget ................................................................................................... 24
2.3.2 Changes in Top of Atmosphere Radiation Budget ....................................................................... 25
2.3.3 Changes in Surface Radiation Budget ......................................................................................... 26
Box 2.3: Global Atmospheric Reanalyses .................................................................................................... 29
2.4 Changes in Temperature ...................................................................................................................... 31
2.4.1 Land-Surface Air Temperature .................................................................................................... 31
2.4.2 Sea Surface Temperature and Marine Air Temperature.............................................................. 35
2.4.3 Global Combined Land and Sea Surface Temperature ............................................................... 38
2.4.4 Upper Air Temperature................................................................................................................ 39
FAQ 2.1: How do We Know the World has Warmed? .............................................................................. 43
2.5 Changes in Hydrological Cycle ............................................................................................................ 44
2.5.1 Large Scale Changes in Precipitation ......................................................................................... 45
2.5.2 Streamflow and Runoff................................................................................................................. 48
2.5.3 Evapotranspiration Including Pan Evaporation.......................................................................... 48
2.5.4 Surface Humidity ......................................................................................................................... 49
2.5.5 Tropospheric Humidity ................................................................................................................ 50
2.5.6 Clouds .......................................................................................................................................... 52
2.6 Changes in Extreme Events .................................................................................................................. 53
2.6.1 Temperature Extremes ................................................................................................................. 53
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2.6.2 Extremes of the Hydrological Cycle ............................................................................................ 56
2.6.3 Tropical Storms............................................................................................................................ 60
2.6.4 Extratropical Storms .................................................................................................................... 61
Box 2.4: Extremes Indices ............................................................................................................................. 62
FAQ 2.2: Have there been any Changes in Climate Extremes? ................................................................ 64
2.7 Changes in Atmospheric Circulation and Patterns of Variability .................................................... 66
2.7.1 Sea Level Pressure ....................................................................................................................... 66
2.7.2 Surface Wind Speed ..................................................................................................................... 67
2.7.3 Upper-Air Winds .......................................................................................................................... 69
2.7.4 Tropospheric Geopotential Height and Tropopause ................................................................... 69
2.7.5 Tropical Circulation .................................................................................................................... 69
2.7.6 Jets, Storm Tracks and Weather Types ........................................................................................ 72
2.7.7 Stratospheric Circulation............................................................................................................. 73
2.7.8 Changes in Indices of Climate Variability ................................................................................... 73
Box 2.5: Patterns and Indices of Climate Variability................................................................................. 76
References ...................................................................................................................................................... 78
Tables ............................................................................................................................................................ 109
Figures .......................................................................................................................................................... 115
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Executive Summary
The evidence of climate change from observations of the atmosphere and surface has grown significantly
during recent years. At the same time new improved ways of characterizing and quantifying uncertainty have
highlighted the challenges that remain for developing long-term, global and regional climate quality data
records. Currently, the observations of the atmosphere and surface indicate the following changes:
Atmospheric Composition
It is certain that atmospheric burdens of the well-mixed greenhouse gases targeted by the Kyoto
Protocol increased from 2005 to 2011. The atmospheric abundance of CO2 was 390.5 ppm (390.3–390.7)1
in 2011; this is 40% greater than in 1750. Atmospheric N2O was 324.2 ppb (324.0–324.4) in 2011 and has
increased by 20% since 1750. Average annual increases in CO2 and N2O from 2005 to 2011 are comparable
to those observed from 1996 to 2005. Atmospheric CH4 was 1803.2 ppb (1801.2–1805.2) in 2011; this is
150% greater than before 1750. CH4 began increasing in 2007 after remaining nearly constant from 1999 to
2006. HFCs, PFCs, and SF6 all continue to increase relatively rapidly, but their contributions to radiative
forcing are less than 1% of the total by well-mixed greenhouse gases. [2.2.1.1]
For ozone depleting substances (Montreal Protocol gases), it is certain that the global mean
abundances of major CFCs are decreasing and HCFCs are increasing. Atmospheric burdens of major
CFCs and halons have decreased since 2005. HCFCs, which are transitional substitutes for CFCs, continue to
increase, but the spatial distribution of their emissions is changing. [2.2.1.2]
Because of large variability and relatively short data records, confidence2 in stratospheric H2O vapour
trends is low. Near-global satellite measurements of stratospheric water vapour show substantial variability
but small net changes for 1992–2011. [2.2.2.1]
It is certain that global stratospheric ozone has declined from pre-1980 values. Most of the decline
occurred prior to the mid 1990s; since then ozone has remained nearly constant at about 3.5% below the
1964-1980 level. [2.2.2.2]
Confidence is medium in large-scale increases of tropospheric ozone across the Northern Hemisphere
since the 1970s. Confidence is low in ozone changes across the Southern Hemisphere due to limited
measurements. It is likely3 that surface ozone trends in eastern North America and western Europe since
2000 have levelled off or decreased and that surface ozone strongly increased in East Asia since the 1990s.
Satellite and surface observations of ozone precursor gases NOx, CO, and non-methane volatile organic
carbons indicate strong regional differences in trends. Most notably NO2 has likely decreased by 30–50% in
Europe and North America and increased by more than a factor of 2 in Asia since the mid-1990s. [2.2.2.3,
2.2.2.4]
It is very likely that aerosol column amounts have declined over Europe and the eastern USA since the
mid 1990s and increased over eastern and southern Asia since 2000. These shifting aerosol regional
patterns have been observed by remote sensing of Aerosol Optical Depth (AOD), a measure of total
atmospheric aerosol load. Declining aerosol loads over Europe and North America are consistent with
1
Values in parentheses are 90% confidence intervals. Elsewhere in this chapter usually the half-widths of the 90%
confidence intervals are provided for the estimated changes from the trend method.
2
In this Report, the following summary terms are used to describe the available evidence: limited, medium, or robust;
and for the degree of agreement: low, medium, or high. A level of confidence is expressed using five qualifiers: very
low, low, medium, high, and very high, and typeset in italics, e.g., medium confidence. For a given evidence and
agreement statement, different confidence levels can be assigned, but increasing levels of evidence and degrees of
agreement are correlated with increasing confidence (see Section 1.4 and Box TS.1 for more details).
3
In this Report, the following terms have been used to indicate the assessed likelihood of an outcome or a result:
Virtually certain 99–100% probability, Very likely 90–100%, Likely 66–100%, About as likely as not 33–66%,
Unlikely 0–33%, Very unlikely 0–10%, Exceptionally unlikely 0–1%. Additional terms (Extremely likely: 95–100%,
More likely than not >50–100%, and Extremely unlikely 0–5%) may also be used when appropriate. Assessed
likelihood is typeset in italics, e.g., very likely (see Section 1.4 and Box TS.1 for more details).
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ground-based in situ monitoring of particulate mass. Confidence in satellite based global average AOD
trends is low. [2.2.3]
Radiation Budgets
Satellite records of Top-of-Atmosphere radiation fluxes have been substantially extended since AR4,
and it is unlikely that significant trends exist in global and tropical radiation budgets since 2000.
Interannual variability in the Earth’s energy imbalance related to El Niño Southern Oscillation is consistent
with ocean heat content records within observational uncertainty. [2.3.2]
Surface solar radiation likely underwent widespread decadal changes after 1950, with decreases
(‘dimming’) until the 1980s and subsequent increases (‘brightening’) observed at many land-based
sites. There is medium confidence for increasing downward thermal and net radiation at land-based
observation sites since the early 1990s. [2.3.3]
Temperature
It is certain that Global Mean Surface Temperature has increased since the late 19th century. Each of
the past three decades has been significantly warmer than all the previous decades in the instrumental
record, and the first decade of the 21st century has been the warmest. The global combined land and
ocean surface temperature data show an increase of about 0.89°C (0.69°C–1.08°C) over the period 1901–
2012 and about 0.72°C (0.49°C–0.89°C) over the period 1951–2012 when described by a linear trend.
Despite the robust multi-decadal timescale warming, there exists substantial multi-annual variability in the
rate of warming with several periods exhibiting almost no linear trend including the warming hiatus since
1998. The rate of warming over 1998–2012 (0.05°C [–0.05 to +0.15] per decade) is smaller than the trend
since 1951 (0.12°C [0.08 to 0.14] per decade). Several independently analyzed data records of global and
regional Land-Surface Air Temperature (LSAT) obtained from station observations are in broad agreement
that LSAT has increased. Sea Surface Temperatures (SSTs) have also increased. Intercomparisons of new
SST data records obtained by different measurement methods, including satellite data, have resulted in better
understanding of uncertainties and biases in the records. [2.4.1, 2.4.2, 2.4.3, Box 9.2]
It is unlikely that any uncorrected urban heat-island effects and land use change effects have raised the
estimated centennial globally averaged LSAT trends by more than 10% of the reported trend. This is
an average value; in some regions with rapid development, urban heat island and land use change impacts on
regional trends may be substantially larger. [2.4.1.3]
Confidence is medium in reported decreases in observed global Diurnal Temperature Range (DTR),
noted as a key uncertainty in the AR4. Several recent analyses of the raw data on which many previous
analyses were based point to the potential for biases that differently affect maximum and minimum average
temperatures. However, apparent changes in DTR are much smaller than reported changes in average
temperatures and therefore it is virtually certain that maximum and minimum temperatures have increased
since 1950. [2.4.1.2]
Based upon multiple independent analyses of measurements from radiosondes and satellite sensors it
is virtually certain that globally the troposphere has warmed and the stratosphere has cooled since the
mid-20th century. Despite unanimous agreement on the sign of the trends, substantial disagreement exists
among available estimates as to the rate of temperature changes, particularly outside the Northern
Hemisphere extra-tropical troposphere, which has been well sampled by radiosondes. Hence there is only
medium confidence in the rate of change and its vertical structure in the Northern Hemisphere extra-tropical
troposphere and low confidence elsewhere. [2.4.4]
Hydrological Cycle
Confidence in precipitation change averaged over global land areas is low prior to 1950 and medium
afterwards because of insufficient data, particularly in the earlier part of the record. Available globally
incomplete records show mixed and nonsignificant long-term trends in reported global mean changes.
Further, when virtually all the land area is filled in using a reconstruction method, the resulting time series
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shows little change in land-based precipitation since 1900. Northern Hemisphere mid-latitude land areas do
show a likely overall increase in precipitation (medium confidence prior to 1950, but high confidence
afterwards). [2.5.1.1]
It is very likely that global near surface and tropospheric air specific humidity have increased since the
1970s. However, during recent years the near surface moistening over land has abated (medium confidence).
As a result, fairly widespread decreases in relative humidity near the surface are observed over the land in
recent years. [2.4.4, 2.5.5, 2.5.6]
While trends of cloud cover are consistent between independent datasets in certain regions, substantial
ambiguity and therefore low confidence remains in the observations of global-scale cloud variability
and trends. [2.5.7]
Extreme Events
It is very likely that the numbers of cold days and nights have decreased and the numbers of warm
days and nights have increased globally since about 1950. There is only medium confidence that the
length and frequency of warm spells, including heat waves, has increased since the middle of the 20th
century mostly due to lack of data or studies in Africa and South America. However, it is likely that
heatwave frequency has increased during this period in large parts of Europe, Asia and Australia. [2.6.1]
It is likely that since about 1950 the number of heavy precipitation events over land has increased in
more regions than it has decreased. Regional trends vary but confidence is highest for central North
America with very likely trends towards heavier precipitation events. [2.6.2.1]
Confidence is low for a global-scale observed trend in drought or dryness (lack of rainfall) since the
middle of the 20th century, due to lack of direct observations, methodological uncertainties and
geographical inconsistencies in the trends. Based on updated studies, AR4 conclusions regarding global
increasing trends in drought since the 1970s were probably overstated. However, this masks important
regional changes: the frequency and intensity of drought has likely increased in the Mediterranean and West
Africa and likely decreased in central North America and north-west Australia since 1950. [2.6.2.2]
Confidence remains low for long-term (centennial) changes in tropical cyclone activity, after
accounting for past changes in observing capabilities. However, it is virtually certain that the frequency
and intensity of the strongest tropical cyclones in the North Atlantic has increased since the 1970s. [2.6.3]
Confidence in large scale trends in storminess or storminess proxies over the last century is low due to
inconsistencies between studies or lack of long-term data in some parts of the world (particularly in
the Southern Hemisphere). [2.6.4]
Because of insufficient studies and data quality issues confidence is also low for trends in small-scale
severe weather events such as hail or thunderstorms. [2.6.2.2]
Atmospheric Circulation and Indices of Variability
It is likely that circulation features have moved poleward since the 1970s, involving a widening of the
tropical belt, a poleward shift of storm tracks and jet streams, and a contraction of the northern polar
vortex. Evidence is more robust for the Northern Hemisphere. It is likely that the Southern Annular Mode
has become more positive since the 1950s. [2.7.5, 2.7.6, 2.7.8, Box 2.5]
Large variability on interannual to decadal time scales hampers robust conclusions on long-term
changes in atmospheric circulation in many instances. Confidence is high that the increase in the northern
mid-latitude westerly winds and the NAO index from the 1950s to the 1990s and the weakening of the
Pacific Walker circulation from the late 19th century to the 1990s have been largely offset by recent changes.
[2.7.5, 2.7.8, Box 2.5]
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Confidence in the existence of long-term changes in remaining aspects of the global circulation is low
due to observational limitations or limited understanding. These include surface winds over land, the
East Asian summer monsoon circulation, the tropical cold-point tropopause temperature and the strength of
the Brewer Dobson circulation. [2.7.2, 2.7.4, 2.7.5, 2.7.7]
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2.1
Chapter 2
IPCC WGI Fifth Assessment Report
Introduction
This chapter assesses the scientific literature on atmospheric and surface observations since AR4 (IPCC,
2007). The most likely changes in physical climate variables or climate forcing agents are identified based on
current knowledge, following the IPCC AR5 uncertainty guidance (Mastrandrea et al., 2011).
As described in AR4 (Trenberth et al., 2007), the climate comprises a variety of space and time-scales: from
the diurnal cycle, to interannual variability such as the El Niño Southern Oscillation (ENSO), to multidecadal variations. ‘Climate change’ refers to a change in the state of the climate that can be identified by
changes in the mean and/or the variability of its properties and that persists for an extended period of time
(Annex III: Glossary). In this chapter, climate change is examined for the period with instrumental
observations, since about 1850. Change prior to this date is assessed in Chapter 5. The word ‘trend’ is used
to designate a long-term movement in a time series that may be regarded, together with the oscillation and
random component, as composing the observed values (Annex III: Glossary). Where numerical values are
given, they are equivalent linear changes (Box 2.2), though more complex non-linear changes in the variable
will often be clear from the description and plots of the time series.
In recent decades, advances in the global climate observing system have contributed to improved monitoring
capabilities. In particular satellites provide additional observations of climate change, which have been
assessed in this and subsequent chapters together with more traditional ground-based and radiosonde
observations. Since AR4, substantial developments have occurred including the production of revised
datasets, more digital data records, and new dataset efforts. New dynamical reanalysis datasets of the global
atmosphere have been published (Box 2.3). These various innovations have improved understanding of data
issues and uncertainties (Box 2.1).
Developing homogeneous long-term records from these different sources remains a challenge. The longest
observational series are land surface air temperatures and sea surface temperatures. Like all physical climate
system measurements they suffer from non-climatic artefacts that must be taken into account (Box 2.1). The
global combined land and sea surface temperature remains an important climate change measure for several
reasons. Climate sensitivity is typically assessed in the context of Global Mean Surface Temperature
(GMST) responses to a doubling of CO2 (Chapter 8) and GMST is thus a key metric in the climate change
policy framework. Also, because it extends back in time farther than any other instrumental series, GMST is
key to understanding both the causes of change and the patterns, role and magnitude of natural variability
(Chapter 10). Starting at various points in the 20th century, additional observations, including balloon-borne
measurements and satellite measurements, and reanalysis products allow analyses of indicators such as
atmospheric composition, radiation budgets, hydrological cycle changes, extreme event characterizations and
circulation indices. A full understanding of the climate system characteristics and changes requires analyses
of all such variables as well as ocean (Chapter 3) and cryosphere (Chapter 4) indicators. Through such a
holistic analysis, a clearer and more robust assessment of the changing climate system emerges (FAQ 2.1).
This chapter starts with an assessment of the observations of the abundances of greenhouse gases (GHGs)
and of aerosols, the main drivers of climate change (Section 2.2). Global trends in GHGs are indicative of
the imbalance between sources and sinks in GHG budgets, and play an important role in emissions
verification on a global scale. The radiative forcing effects of GHGs and aerosols are assessed in Chapter 8.
The observed changes in radiation budgets are discussed in Section 2.3. Aerosol-cloud interactions are
assessed in Chapter 7. Section 2.4 provides an assessment of observed changes in surface and atmospheric
temperature. Observed change in the hydrological cycle, including precipitation and clouds, is assessed in
Section 2.5. Changes in variability and extremes (such as cold spells, heat waves, droughts and tropical
cyclones) are assessed in Section 2.6. Section 2.7 assesses observed changes in the circulation of the
atmosphere and its modes of variability, which help determine seasonal and longer-term anomalies at
regional scales (Chapter 14).
Trends have been assessed where possible for multi-decadal periods starting in 1880, 1901 (referred to as
long-term trends) and in 1951, 1979 (referred to as short-term trends). The time elapsed since AR4 extends
the period for trend calculation from 2005 to 2012 for many variables. The global temperature trend since
1998 has also been considered (see also Box 9.2) as well as the trends for sequential 30-year segments of the
time series. For many variables derived from satellite data, information is available for 1979–2012 only. In
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general, trend estimates are more reliable for longer time intervals, and trends computed on short intervals
have a large uncertainty. Trends for short intervals are very sensitive to the start and end years. An exception
to this is trends in greenhouse gases, whose accurate measurement and long lifetimes make them well-mixed
and less susceptible to year-to-year variability, so that trends computed on relatively short intervals are very
meaningful for these variables. Where possible, the time interval 1961–1990 has been chosen as the
climatological reference period (or normal period) for averaging. This choice enables direct comparisons
with AR4, but is different from the present-day climate period (1986–2005) used as a reference in the
modelling chapters of AR5 and Annex I: Atlas of Global and Regional Climate Projections.
It is important to note that the question of whether the observed changes are outside the possible range of
natural internal climate variability and consistent with the climate effects from changes in atmospheric
composition is not addressed in this chapter, but rather in Chapter 10. No attempt has been undertaken to
further describe and interpret the observed changes in terms of multidecadal oscillatory (or low frequency)
variations, (long-term) persistence and/or secular trends (e.g., as in Cohn and Lins, 2005; Koutsoyiannis and
Montanari, 2007; Zorita et al., 2008; Lennartz and Bunde, 2009; Mills, 2010; Mann, 2011; Wu et al., 2011;
Zhou and Tung, 2012; Tung and Zhou, 2013). In this chapter, the robustness of the observed changes is
assessed in relation to various sources of observational uncertainty (Box 2.1). In addition, the reported trend
significance and statistical confidence intervals provide an indication of how large the observed trend is
compared to the range of observed variability in a given aspect of the climate system (see Box 2.2 for a
description of the statistical trend model applied). Unless otherwise stated, 90% confidence intervals are
given. The chapter also examines the physical consistency across different observations, which helps to
provide additional confidence in the reported changes. Additional information about data sources and
methods is described in the Supplementary Material to Chapter 2.
[START BOX 2.1 HERE]
Box 2.1: Uncertainty in Observational Records
The vast majority of historical (and modern) weather observations were not made explicitly for climate
monitoring purposes. Measurements have changed in nature as demands on the data, observing practices and
technologies have evolved. These changes almost always alter the characteristics of observational records,
changing their mean, their variability or both, such that it is necessary to process the raw measurements
before they can be considered useful for assessing the true climate evolution. This is true of all observing
techniques that measure physical atmospheric quantities. The uncertainty in observational records
encompasses instrumental / recording errors, effects of representation (e.g., exposure, observing frequency or
timing), as well as effects due to physical changes in the instrumentation (such as station relocations or new
satellites). All further processing steps (transmission, storage, gridding, interpolating, averaging) also have
their own particular uncertainties. Since there is no unique, unambiguous, way to identify and account for
non-climatic artefacts in the vast majority of records, there must be a degree of uncertainty as to how the
climate system has changed. The only exceptions are certain atmospheric composition and flux
measurements whose measurements and uncertainties are rigorousy tied through an unbroken chain to
internationally recognized absolute measurement standards (e.g., the CO2 record at Mauna Loa; Keeling et
al., 1976a).
Uncertainty in dataset production can result either from the choice of parameters within a particular
analytical framework - parametric uncertainty, or from the choice of overall analytical framework - structural
uncertainty. Structural uncertainty is best estimated by having multiple independent groups assess the same
data using distinct approaches. More analyses assessed now than in AR4 include published estimates of
parametric or structural uncertainty. It is important to note that the literature includes a very broad range of
approaches. Great care has been taken in comparing the published uncertainty ranges as they almost always
do not constitute a like-for-like comparison. In general, studies that account for multiple potential error
sources in a rigorous manner yield larger uncertainty ranges. This yields an apparent paradox in
interpretation as one might think that smaller uncertainty ranges should indicate a better product. However,
in many cases this would be an incorrect inference as the smaller uncertainty range may instead reflect that
the published estimate considered only a subset of the plausible sources of uncertainty. Within the timeseries
figures, where this issue would be most acute, such parametric uncertainty estimates are therefore not
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generally included. Consistent with AR4 HadCRUT4 uncertainties in GMST are included in Figure 2.19,
which in addition includes structural uncertainties in GMST.
To conclude, the vast majority of the raw observations used to monitor the state of the climate contain
residual non-climatic influences. Removal of these influences cannot be done definitively and neither can the
uncertainties be unambiguously assessed. Therefore, care is required in interpreting both data products and
their stated uncertainty estimates. Confidence can be built from: redundancy in efforts to create products;
dataset heritage; and cross-comparisons of variables that would be expected to co-vary for physical reasons,
such as land surface temperatures and sea surface temperatures around coastlines. Finally, trends are often
quoted as a way to synthesize the data into a single number. Uncertainties that arise from such a process and
the choice of technique used within this chapter are described in more detail in Box 2.2.
[END BOX 2.1 HERE]
2.2
2.2.1
Changes in Atmospheric Composition
Well-Mixed Greenhouse Gases
AR4 (Forster et al., 2007; IPCC, 2007) concluded that increasing atmospheric burdens of well-mixed
greenhouse gases (GHG) resulted in a 9% increase in their radiative forcing from 1998 to 2005. Since 2005,
the atmospheric abundances of many well-mixed GHG increased further, but the burdens of some ozone
depleting substances (ODS) whose production and use were controlled by the Montreal Protocol on
Substances that Deplete the Ozone Layer (1987; hereinafter, ‘Montreal Protocol’) decreased.
Based on updated in situ observations, this assessment concludes that these trends resulted in a 7.5% increase
in radiative forcing from greenhouse gases from 2005 to 2011, with CO2 contributing 80%. Of note is an
increase in the average growth rate of atmospheric CH4 from ~0.5 ppb yr–1 during 1999–2006 to ~6 ppb yr–1
from 2007 through 2011. Current observation networks are sufficient to quantify global annual mean
burdens used to calculate radiative forcing and to constrain global emission rates (with knowledge of loss
rates), but they are not sufficient for accurately estimating regional scale emissions and how they are
changing with time.
The globally, annually averaged well-mixed GHG mole fractions reported here are used in Chapter 8 to
calculate radiative forcing. A direct, inseparable connection exists between observed changes in atmospheric
composition and well-mixed GHG emissions and losses (discussed in Chapter 6 for CO2, CH4, and N2O). A
global GHG budget consists of the total atmospheric burden, total global rate of production or emission (i.e.,
sources), and the total global rate of destruction or removal (i.e., sinks). Precise, accurate systematic
observations from independent globally distributed measurement networks are used to estimate global annual
mean well-mixed GHG mole fractions at Earth’s surface, and these allow estimates of global burdens.
Emissions are predominantly from surface sources, which are described in Chapter 6 for CO2, CH4, and N2O.
Direct use of observations of well-mixed GHG to model their regional budgets can also play an important
role in verifying inventory estimates of emissions (Nisbet and Weiss, 2010).
Systematic measurements of well-mixed GHG in ambient air began at various times during the last six
decades, with earlier atmospheric histories being reconstructed from measurements of air stored in air
archives and trapped in polar ice cores or in firn. In contrast to the physical meteorological parameters
discussed elsewhere in this chapter, measurements of well-mixed GHG are reported relative to standards
developed from fundamental SI base units (SI = International System of Units) as dry-air mole fractions, a
unit that is conserved with changes in temperature and pressure (Box 2.1). This eliminates dilution by H2O
vapour, which can reach 4% of total atmospheric composition. Here, the following abbreviations are used:
ppm = µmol mol–1; ppb = nmol mol–1; and ppt = pmol mol–1. Unless noted otherwise, an average of NOAA
and AGAGE annually averaged surface global mean mole fractions are described in Section 2.2.1 (see
Supplementary Material 2.SM.2 for further species not listed here).
Table 2.1 summarizes globally, annually averaged well-mixed GHG mole fractions from four independent
measurement programs. Sampling strategies and techniques for estimating global means and their
uncertainties vary among programs. Differences among measurement programs are relatively small and will
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not add significantly to uncertainty in radiative forcing. Time series of the well-mixed GHG are plotted in
Figures 2.1 (CO2), 2.2 (CH4), 2.3 (N2O), and 2.4 (halogen-containing compounds).
[INSERT TABLE 2.1 HERE]
Table 2.1: Global annual mean surface dry-air mole fractions and their change since 2005 for well-mixed GHG from
four measurement networks. Units are ppt except where noted. Uncertainties are 90% confidence intervals. aREs
(radiative efficiency) and lifetimes (except CH4 and N2O, which are from Prather et al. (2012)) are from Chapter 8.
2.2.1.1
Kyoto Protocol Gases (CO2, CH4, N2O, HFCs, PFCs, and SF6)
2.2.1.1.1 Carbon dioxide (CO2)
Precise, accurate systematic measurements of atmospheric CO2 at Mauna Loa, Hawaii and South Pole were
started by C. D. Keeling from Scripps Institution of Oceanography in the late 1950s (Keeling et al., 1976a;
Keeling et al., 1976b). The 1750 globally averaged abundance of atmospheric CO2 based on measurements
of air extracted from ice cores and from firn is 278 ± 2 ppm (Etheridge et al., 1996). Globally averaged CO2
mole fractions since the start of the instrumental record are plotted in Figure 2.1. The main features in the
contemporary CO2 record are the long-term increase and the seasonal cycle resulting from photosynthesis
and respiration by the terrestrial biosphere, mostly in the NH. The main contributors to increasing
atmospheric CO2 abundance are fossil fuel combustion and land use change (Section 6.3). Multiple lines of
observational evidence indicate that during the past few decades, most of the increasing atmospheric burden
of CO2 is from fossil fuel combustion (Tans, 2009). Since the last year for which the AR4 reported (2005),
CO2 has increased by 11.7 ppm to 390.5 ppm in 2011 (Table 2.1). From 1980 to 2011, the average annual
increase in globally averaged CO2 (from 1 January in one year to 1 January in the next year) was 1.7 ppm yr–
1
(1 standard deviation = 0.5 ppm yr–1; 1 ppm globally corresponds to 2.1 PgC increase in the atmospheric
burden). Since 2001, CO2 has increased at 2.0 ppm yr–1 (1 standard deviation = 0.3 ppm yr–1). The CO2
growth rate varies from year to year; since 1980 the range in annual increase is 0.7 ± 0.1 ppm in 1992 to 2.9
± 0.1 ppm in 1998. Most of this interannual variability in growth rate is driven by small changes in the
balance between photosynthesis and respiration on land, each having global fluxes of ~120 PgC yr–1
(Chapter 6).
[INSERT FIGURE 2.1 HERE]
Figure 2.1: a) Globally averaged CO2 dry-air mole fractions from Scripps Institution of Oceanography (SIO) at
monthly time resolution based on measurements from Mauna Loa, Hawaii and South Pole (red) and
NOAA/ESRL/GMD at quasi-weekly time resolution (blue). SIO values are deseasonalized. b) Instantaneous growth
rates for globally averaged atmospheric CO2 using the same colour code as in (a). Growth rates are calculated as the
time derivative of the deseasonalized global averages (Dlugokencky et al., 1994).
2.2.1.1.2 Methane (CH4)
Globally averaged CH4 in 1750 was 722 ± 25 ppb (after correction to the NOAA-2004 CH4 standard scale)
(Etheridge et al., 1998; Dlugokencky et al., 2005), although human influences on the global CH4 budget may
have began thousands of years earlier than this time that is normally considered “pre-industrial” (Ruddiman,
2003; Ferretti et al., 2005; Ruddiman, 2007). In 2011, the global annual mean was 1803 ± 2 ppb. Direct
atmospheric measurements of CH4 of sufficient spatial coverage to calculate global annual means began in
1978 and are plotted through 2011 in Figure 2.2a. This time period is characterized by a decreasing growth
rate (Figure 2.2b) from the early 1980s until 1998, stabilization from 1999 to 2006, and an increasing
atmospheric burden from 2007 to 2011 (Rigby et al., 2008; Dlugokencky et al., 2009). Assuming no longterm trend in hydroxyl radical (OH) concentration, the observed decrease in CH4 growth rate from the early
1980s through 2006 indicates an approach to steady state where total global emissions have been
approximately constant at ~550 TgCH4 yr–1. Superimposed on the long-term pattern is significant interannual
variability; studies of this variability are used to improve understanding of the global CH4 budget (Chapter
6). The most likely drivers of increased atmospheric CH4 were anomalously high temperatures in the Arctic
in 2007 and greater than average precipitation in the tropics during 2007 and 2008 (Dlugokencky et al.,
2009; Bousquet, 2011). Observations of the difference in CH4 between zonal averages for northern and
southern polar regions (53°–90°) (Dlugokencky et al., 2009; Dlugokencky et al., 2011) suggest that, so far, it
is unlikely that there has been a permanent measureable increase in Arctic CH4 emissions from wetlands and
shallow sub-sea CH4 clathrates.
[INSERT FIGURE 2.2 HERE]
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Figure 2.2: a) Globally averaged CH4 dry-air mole fractions from UCI (green; 4 values per year, except prior to 1984,
when they are of lower and varying frequency), AGAGE (red; monthly), and NOAA/ESRL/GMD (blue; quasi-weekly).
b) Instantaneous growth rate for globally averaged atmospheric CH4 using the same colour code as in (a). Growth rates
were calculated as in Figure 2.1.
Reaction with OH is the main loss process for CH4 (and for HFCs and HCFCs), and it is the largest term in
the global CH4 budget. Therefore, trends and interannual variability in OH concentration significantly impact
our understanding of changes in CH4 emissions. CH3CCl3 (Section 2.2.1.2) has been used extensively to
estimate globally averaged OH concentrations (e.g., Prinn et al., 2005). AR4 reported no trend in [OH] from
1979 to 2004, and there is no evidence from this assessment to change that conclusion for 2005 to 2011.
Montzka et al. (2011a) exploited the exponential decrease and small emissions in CH3CCl3 to show that
interannual variations in OH concentration from 1998 to 2007 are 2.3 ± 1.5%, which is consistent with
estimates based on CH4, C2Cl4, CH2Cl2, CH3Cl, and CH3Br.
2.2.1.1.3 Nitrous oxide (N2O)
Globally averaged N2O in 2011 was 324.2 ppb, an increase of 5.0 ppb over the value reported for 2005 in
AR4 (Table 2.1). This is an increase of 20% over the estimate for 1750 from ice cores, 270 ± 7 ppb (Prather
et al., 2012). Measurements of N2O and its isotopic composition in firn air suggest the increase, at least since
the early 1950s, is dominated by emissions from soils treated with synthetic and organic (manure) nitrogen
fertilizer (Rockmann and Levin, 2005; Ishijima et al., 2007; Davidson, 2009; Syakila and Kroeze, 2011).
Since systematic measurements began in the late 1970s, N2O has increased at an average rate of ~0.75 ppb
yr–1 (Figure 2.3). Because the atmospheric burden of CFC-12 is decreasing, N2O has replaced CFC-12 as the
third most important well-mixed GHG contributing to radiative forcing (Elkins and Dutton, 2011).
[INSERT FIGURE 2.3 HERE]
Figure 2.3: a) Globally averaged N2O dry-air mole fractions from AGAGE (red) and NOAA/ESRL/GMD (blue) at
monthly resolution. b) Instantaneous growth rates for globally averaged atmospheric N2O. Growth rates were calculated
as in Figure 2.1.
Persistent latitudinal gradients in annually averaged N2O are observed at background surface sites, with
maxima in the northern subtropics, values about 1.7 ppb lower in the Antarctic, and values about 0.4 ppb
lower in the Arctic (Huang et al., 2008). These persistent gradients contain information about anthropogenic
emissions from fertilizer use at northern tropical to mid-latitudes and natural emissions from soils and ocean
upwelling regions of the tropics. N2O time series also contain seasonal variations with peak-to-peak
amplitudes of about 1 ppb in high latitudes of the NH and about 0.4 ppb at high southern and tropical
latitudes. In the NH, exchange of air between the stratosphere (where N2O is destroyed by photochemical
processes) and troposphere is the dominant contributor to observed seasonal cycles, not seasonality in
emissions (Jiang et al., 2007). Nevison et al. (2011) found correlations between the magnitude of detrended
N2O seasonal minima and lower stratospheric temperature providing evidence for a stratospheric influence
on the timing and amplitude of the seasonal cycle at surface monitoring sites. In the Southern Hemisphere
(SH), observed seasonal cycles are also affected by stratospheric influx, and by ventilation and thermal outgassing of N2O from the oceans.
2.2.1.1.4 HFCs, PFCs, SF6, and NF3
The budgets of HFCs, PFCs, and SF6 were recently reviewed in Chapter 1 of the Scientific Assessment of
Ozone Depletion: 2010 (Montzka et al., 2011b), so only a brief description is given here. The current
atmospheric abundances of these species are summarized in Table 2.1 and plotted in Figure 2.4.
[INSERT FIGURE 2.4 HERE]
Figure 2.4: Globally averaged dry-air mole fractions at Earth’s surface of the major halogen-containing well-mixed
GHG. These are derived mainly using monthly mean measurements from the AGAGE and NOAA/ESRL/GMD
networks. For clarity, only the most abundant chemicals are shown in different compound classes and results from
different networks have been combined when both are available.
Atmospheric HFC abundances are low and their contribution to radiative forcing is small relative to the
CFCs and HCFCs they replace (less than 1% of the total by well-mixed greenhouse gases; Chapter 8). As
they replace CFCs and HCFCs phased out by the Montreal Protocol, however, their contribution to future
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climate forcing is projected to grow considerably in the absence of controls on global production (Velders et
al., 2009).
HFC-134a is a replacement for CFC-12 in automobile air conditioners and is also used in foam blowing
applications. In 2011, it reached 62.7 ppt, an increase of 28.2 ppt since 2005. Based on analysis of highfrequency measurements, the largest emissions occur in N. America, Europe, and East Asia (Stohl et al.,
2009).
HFC-23 is a by-product of HCFC-22 production. Direct measurements of HFC-23 in ambient air at 5 sites
began in 2007. The 2005 global annual mean used to calculate the increase since AR4 in Table 2.1, 5.2 ppt,
is based on an archive of air collected at Cape Grim, Tasmania (Miller et al., 2010). In 2011, atmospheric
HFC-23 was at 24.0 ppt. Its growth rate peaked in 2006 as emissions from developing countries increased,
then declined as emissions were reduced through abatement efforts under the Clean Development
Mechanism (CDM) of the UNFCCC. Estimates of total global emissions based on atmospheric observations
and bottom-up inventories agree within uncertainties (Miller et al., 2010; Montzka et al., 2010). Currently,
the largest emissions of HFC-23 are from east Asia (Yokouchi et al., 2006; Kim et al., 2010; Stohl et al.,
2010); developed countries emit <20% of the global total. Keller et al. (2011) found that emissions from
developed countries may be larger than those reported to the UNFCCC, but their contribution is small. The
lifetime of HFC-23 was revised from 270 to 222 years since AR4.
After HFC-134a and HFC-23, the next most abundant HFCs are HFC-143a at 12.04 ppt in 2011, 6.39 ppt
greater than in 2005; HFC-125 (O'Doherty et al., 2009) at 9.58 ppt, increasing by 5.89 ppt since 2005; HFC152a (Greally et al., 2007) at 6.4 ppt with a 3.0 ppt increase since 2005; and HFC-32 at 4.92 ppt in 2011,
3.77 ppt greater than in 2005. Since 2005, all of these were increasing exponentially except for HFC-152a,
whose growth rate slowed considerably in ~2007 (Figure 2.4). HFC-152a has a relatively short atmospheric
lifetime of 1.5 years, so its growth rate will respond quickly to changes in emissions. Its major uses are as a
foam blowing agent and aerosol spray propellant while HFC-143a, HFC-125, and HFC-32 are mainly used
in refrigerant blends. The reasons for slower growth in HFC-152a since ~2007 are unclear. Total global
emissions of HFC-125 estimated from the observations are within ~20% of emissions reported to the
UNFCCC, after accounting for estimates of unreported emissions from east Asia (O'Doherty et al., 2009).
CF4 and C2F6 (PFCs) have lifetimes of 50 kyr and 10 kyr, respectively, and they are emitted as by-products
of aluminium production and used in plasma etching of electronics. CF4 has a natural lithospheric source
(Deeds et al., 2008) with a 1750 level determined from Greenland and Antarctic firn air of 34.7 ± 0.2 ppt
(Worton et al., 2007; Muhle et al., 2010). In 2011, atmospheric abundances were 79.0 ppt for CF4, increasing
by 4.0 ppt since 2005, and 4.16 ppt for C2F6, increasing by 0.50 ppt. The sum of emissions of CF4 reported
by aluminium producers and for non-aluminium production in EDGAR (Emission Database for Global
Atmospheric Research) v4.0 only accounts for about half of global emissions inferred from atmospheric
observations (Muhle et al., 2010). For C2F6, emissions reported to the UNFCCC are also substantially lower
than those estimated from atmospheric observations (Muhle et al., 2010).
The main sources of atmospheric SF6 emissions are electricity distribution systems, magnesium production,
and semi-conductor manufacturing. Global annual mean SF6 in 2011 was 7.29 ppt, increasing by 1.65 ppt
since 2005. SF6 has a lifetime of 3200 years, so its emissions accumulate in the atmosphere and can be
estimated directly from its observed rate of increase. Levin et al. (2010) and Rigby et al. (2010) showed that
SF6 emissions decreased after 1995, most likely because of emissions reductions in developed countries, but
then increased after 1998. During the past decade, they found that actual SF6 emissions from developed
countries are at least twice the reported values.
NF3 was added to the list of GHG in the Kyoto Protocol with the Doha Amendment, December, 2012. Weiss
et al. (2008) determined 0.45 ppt for its global annual mean mole fraction in 2008, growing from almost zero
in 1978. In 2011, NF3 was 0.60 ppt, increasing by 0.30 ppt since 2005. Initial bottom-up inventories
underestimated its emissions; based on the atmospheric observations, NF3 emissions were 1.18 ± 0.21Gg in
2008 (Arnold et al., 2013).
In summary, it is certain that atmospheric burdens of well-mixed greenhouse gases targeted by the Kyoto
Protocol increased from 2005 to 2011. The atmospheric abundance of CO2 was 390.5 ± 0.2 ppm in 2011; this
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is 40% greater than before 1750. Atmospheric N2O was 324.2 ± 0.2 ppb in 2011 and has increased by 20%
since 1750. Average annual increases in CO2 and N2O from 2005 to 2011 are comparable to those observed
from 1996 to 2005. Atmospheric CH4 was 1803.2 ± 2.0 ppb in 2011; this is 150% greater than before 1750.
CH4 began increasing in 2007 after remaining nearly constant from 1999 to 2006. HFCs, PFCs, and SF6 all
continue to increase relatively rapidly, but their contributions to radiative forcing are less than 1% of the total
by well-mixed greenhouse gases (Chapter 8).
2.2.1.2
Ozone Depleting Substances (CFCs, Chlorinated solvents, and HCFCs)
CFC atmospheric abundances are decreasing (Figure 2.4) because of the successful reduction in emissions
resulting from the Montreal Protocol. By 2010, emissions from ODSs had been reduced by ~11 Pg CO2-eq
yr–1, which is 5 to 6 times the reduction target of the first commitment period (2008–2012) of the Kyoto
Protocol (2 PgCO2-eq yr–1) (Velders et al., 2007). These avoided equivalent-CO2 emissions account for the
offsets to radiative forcing by stratospheric O3 depletion caused by ODSs and the use of HFCs as substitutes
for them. Recent observations in Arctic and Antarctic firn air further confirm that emissions of CFCs are
entirely anthropogenic (Martinerie et al., 2009; Montzka et al., 2011b). CFC-12 has the largest atmospheric
abundance and GWP-weighted emissions (which are based on a 100 yr time horizon) of the CFCs. Its
tropospheric abundance peaked during 2000–2004. Since AR4, its global annual mean mole fraction
declined by 13.8 ppt to 528.5 ppt in 2011. CFC-11 continued the decrease that started in the mid-1990s, by
12.9 ppt since 2005. In 2011, CFC-11 was 237.7 ppt. CFC-113 decreased by 4.3 ppt since 2005 to 74.3 ppt
in 2011. A discrepancy exists between top-down and bottom-up methods for calculating CFC-11 emissions
(Montzka et al., 2011b). Emissions calculated using top-down methods come into agreement with bottom-up
estimates when a lifetime of 64 years is used for CFC-11 in place of the accepted value of 45 years; this
longer lifetime (64 years) is at the upper end of the range estimated by Douglass et al. (2008) with models
that more accurately simulate stratospheric circulation. Future emissions of CFCs will largely come from
‘banks’ (i.e., material residing in existing equipment or stores) rather than current production.
The mean decrease in globally, annually averaged CCl4 based on NOAA and AGAGE measurements since
2005 was 7.4 ppt, with an atmospheric abundance of 85.8 ppt in 2011 (Table 2.1). The observed rate of
decrease and inter hemispheric difference of CCl4 suggest that emissions determined from the observations
are on average greater and less variable than bottom-up emission estimates, although large uncertainties in
the CCl4 lifetime result in large uncertainties in the top-down estimates of emissions (Xiao et al., 2010;
Montzka et al., 2011b). CH3CCl3 has declined exponentially for about a decade, decreasing by 12.0 ppt since
2005 to 6.3 ppt in 2011.
HCFCs are classified as ‘transitional substitutes’ by the Montreal Protocol. Their global production and use
will ultimately be phased out, but their global production is not currently capped and, based on changes in
observed spatial gradients, there has likely been a shift in emissions within the NH from regions north of
about 30°N to regions south of 30°N (Montzka et al., 2009). Global levels of the three most abundant
HCFCs in the atmosphere continue to increase. HCFC-22 increased by 44.5 ppt since 2005 to 213.3 ppt in
2011. Developed country emissions of HCFC-22 are decreasing, and the trend in total global emissions is
driven by large increases from south and Southeast Asia (Saikawa et al., 2012). HCFC-141b increased by 3.7
ppt since 2005 to 21.4 ppt in 2011, and for HCFC-142b, the increase was 5.73 ppt to 21.1 ppt in 2011. The
rates of increase in these three HCFCs increased since 2004, but the change in HCFC-141b growth rate was
smaller and less persistent than for the other two, which approximately doubled from 2004 to 2007 (Montzka
et al., 2009).
In summary, for ozone depleting substances, whose production and consumption are controlled by the
Montreal Protocol, it is certain that the global mean abundances of major CFCs are decreasing and HCFCs
are increasing. Atmospheric burdens of CFC-11, CFC-12, CFC-113, CCl4, CH3CCl3, and halons have
decreased since 2005. HCFCs, which are transitional substitutes for CFCs, continue to increase, but the
spatial distribution of their emissions is changing.
2.2.2
Near Term Climate Forcers
This section covers observed trends in stratospheric water vapour; stratospheric and tropospheric ozone (O3);
the O3 precursor gases, nitrogen dioxide (NO2) and carbon monoxide (CO); and column and surface aerosol.
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Since trend estimates from the cited literature are used here, issues such as data records of different length,
potential lack of comparability among measurement methods, and different trend calculation methods, add to
the uncertainty in assessing trends.
2.2.2.1
Stratospheric H2O Vapour
Stratospheric H2O vapour has an important role in Earth’s radiative balance and in stratospheric chemistry.
Increased stratospheric H2O vapour causes the troposphere to warm and the stratosphere to cool (Manabe
and Strickler, 1964; Solomon et al., 2010), and also causes increased rates of stratospheric O3 loss (Stenke
and Grewe, 2005). Water vapour enters the stratosphere through the cold tropical tropopause. As moisturerich air masses are transported through this region, most water vapour condenses resulting in extremely dry
lower stratospheric air. Because tropopause temperature varies seasonally, so does H2O abundance there.
Other contributions include oxidation of methane within the stratosphere, and possibly direct injection of
H2O vapour in overshooting deep convection (Schiller et al., 2009). AR4 reported that stratospheric H2O
vapour showed significant long-term variability and an upward trend over the last half of the 20th century,
but no net increase since 1996. This updated assessment finds large interannual variations that have been
observed by independent measurement techniques, but no significant net changes since 1996.
The longest continuous time series of stratospheric water vapour abundance is from in situ measurements
made with frost point hygrometers starting in 1980 over Boulder, USA (40°N, 105°W) (Scherer et al., 2008),
with values ranging from 3.5 to 5.5 ppm, depending on altitude. These observations have been
complemented by long-term global satellite observations from SAGE II (1984–2005; Stratospheric Aerosol
and Gas Experiment II (Chu et al., 1989)), HALOE (1991–2005; HALogen Occultation Experiment
(RUSSELL et al., 1993)), Aura MLS (2004–present; Microwave Limb Sounder (Read et al., 2007)) and
Envisat MIPAS (2002-2012; Michelson Interferometer for Passive Atmospheric Sounding (Milz et al., 2005;
von Clarmann et al., 2009)). Discrepancies in water vapour mixing ratios from these different instruments
can be attributed to differences in the vertical resolution of measurements, along with other factors. For
example, offsets of up to 0.5 ppm in lower stratospheric water vapour mixing ratios exist between the most
current versions of HALOE (v20) and Aura MLS (v3.3) retrievals during their 16-month period of overlap
(2004 to 2005), although such biases can be removed to generate long-term records. Since AR4, new studies
characterize the uncertainties in measurements from individual types of in situ H2O sensors (Vomel et al.,
2007b; Vomel et al., 2007a; Weinstock et al., 2009), but discrepancies between different instruments (50 to
100% at H2O mixing ratios less than 10 ppm), particularly for high-altitude measurements from aircraft,
remain largely unexplained.
Observed anomalies in stratospheric H2O from the near-global combined HALOE+MLS record (1992–2011)
(Figure 2.5) include effects linked to the stratospheric quasi-biennial oscillation (QBO) influence on
tropopause temperatures, plus a step-like drop after 2001 (noted in AR4), and an increasing trend since 2005.
Variability during 2001–2011 was large yet there was only a small net change from 1992 through 2011.
These interannual water vapour variations for the satellite record are closely linked to observed changes in
tropical tropopause temperatures (Fueglistaler and Haynes, 2005; Randel et al., 2006; Rosenlof and Reid,
2008; Randel, 2010), providing reasonable understanding of observed changes. The longer record of Boulder
balloon measurements (since 1980) has been updated and reanalyzed (Scherer et al., 2008; Hurst et al.,
2011), showing decadal-scale variability and a long-term stratospheric (16–26 km) increase of 1.0 ± 0.2 ppm
for 1980–2010. Agreement between interannual changes inferred from the Boulder and HALOE+MLS data
is good for the period since 1998 but was poor during 1992–1996. About 30% of the positive trend during
1980–2010 determined from frost point hygrometer data (Fujiwara et al., 2010; Hurst, 2011) can be
explained by increased production of H2O from CH4 oxidation (Rohs et al., 2006), but the remainder can not
be explained by changes in tropical tropopause temperatures (Fueglistaler and Haynes, 2005) or other known
factors.
In summary, near-global satellite measurements of stratospheric H2O show substantial variability for 1992–
2011, with a step-like decrease after 2000 and increases since 2005. Because of this large variability and
relatively short time series, confidence in long-term stratospheric H2O trends is low. There is good
understanding of the relationship between the satellite-derived H2O variations and tropical tropopause
temperature changes. Stratospheric H2O changes from temporally sparse balloon-borne observations at one
location (Boulder, Colorado) are in good agreement with satellite observations from 1998 to present, but
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discrepancies exist for changes during 1992–1996. Long-term balloon measurements from Boulder indicate
a net increase of 1.0 ± 0.2 ppm over 16–26 km for 1980–2010, but these long-term increases cannot be fully
explained by changes in tropical tropopause temperatures, methane oxidation, or other known factors.
[INSERT FIGURE 2.5 HERE]
Figure 2.5: Water vapour anomalies in the lower stratosphere (~16–19 km) from satellite sensors and in situ
measurements normalized to 2000–2011. (a) Monthly mean water vapour anomalies at 83 hPa for 60°S–60°N (blue)
determined from HALOE and MLS satellite sensors. (b) Approximately monthly balloon-borne measurements of
stratospheric water vapour from Boulder, Colorado at 40°N (green dots; green curve is 15-point running mean)
averaged over 16–18 km and monthly means as in (a), but averaged over 30°N–50°N (black).
2.2.2.2
Stratospheric Ozone
AR4 did not explicitly discuss measured stratospheric ozone trends. For the current assessment report such
trends are relevant because they are the basis for revising the radiative forcing from –0.05 ± 0.10 W m–2 in
1750 to –0.10 ± 0.15 W m–2 in 2005 (Section 8.3.3.2). These values strongly depend on the vertical
distribution of the stratospheric ozone changes.
Total ozone is a good proxy for stratospheric ozone, since tropospheric ozone accounts for only about 10%
of the total ozone column. Long-term total ozone changes over various latitudinal belts, derived from Weber
et al. (2012), are illustrated in Figure 2.6 (a-d). Annually averaged total column ozone declined during the
1980s and early 1990s and has remained constant for the past decade, about 3.5 and 2.5% below the 1964–
1980 average for the entire globe (not shown) and 60°S–60°N, respectively with changes occurring mostly
outside the tropics, particularly the SH where the current extratropical (30ºS–60ºS) mean values are 6%
below the 1964–1980 average, compared to 3.5% for the Northern Hemisphere (NH) extratropics (Douglass
et al., 2011). In the NH, the 1993 minimum of about –6% was primarily caused by ozone loss through
heterogeneous reactions on volcanic aerosols from Mt. Pinatubo.
Two altitude regions are mainly responsible for long-term changes in total column ozone (Douglass et al.,
2011). In the upper stratosphere (35–45 km), there was a strong and statistically significant decline (about
10%) up to the mid-1990s and little change or a slight increase since. The lower stratosphere, between 20
and 25 km over mid-latitudes, also experienced a statistically significant decline (7–8%) between 1979 and
the mid-1990s, followed by stabilization or a slight (2–3%) ozone increase.
Springtime averages of total ozone poleward of 60° latitude in the Arctic and Antarctic are shown in Figure
2.6e. By far the strongest ozone loss in the stratosphere occurs in austral spring over Antarctica (ozone hole)
and its impact on SH climate is discussed in Chapters 11, 12, and 14. Interannual variability in polar
stratospheric ozone abundance and chemistry is driven by variability in temperature and transport due to
year-to-year differences in dynamics. This variability is particularly large in the Arctic where the most recent
large depletion occurred in 2011, when chemical ozone destruction was, for the first time in the
observational record, comparable to that in the Antarctic (Manney et al., 2011).
In summary, it is certain that global stratospheric ozone has declined from pre-1980 values. Most of the
decline occurred prior to the mid-1990s; since then there has been little net change and ozone has remained
nearly constant at about 3.5% below the 1964–1980 level.
[INSERT FIGURE 2.6 HERE]
Figure 2.6: Zonally averaged, annual mean total column ozone in Dobson Units (DU; 1 DU = 2.69 × 1016 O3/cm2) from
ground-based measurements combining Brewer, Dobson, and filter spectrometer data WOUDC (red),
GOME/SCIAMACHY/GOME-2 GSG (green) and merged satellite BUV/TOMS/SBUV/OMI MOD V8 (blue) for a)
Non-Polar Global (60°S–60°N), b) NH (30°N–60°N), c) Tropics (25°S–25°N), d) SH (30°S–60°S) and e) March NH
Polar (60°N–90°N) and October SH Polar. Adapted from Weber et al. (2012; see also for abbreviations).
2.2.2.3
Tropospheric Ozone
Tropospheric ozone is a short-lived trace gas that either originates in the stratosphere or is produced in situ
by precursor gases and sunlight (e.g., Monks et al., 2009). An important greenhouse gas with an estimated
radiative forcing of 0.40 ± 0.20 W m–2 (Chapter 8), tropospheric ozone also impacts human health and
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vegetation at the surface. Its average atmospheric lifetime of a few weeks produces a global distribution
highly variable by season, altitude and location. These characteristics and the paucity of long-term
measurements make the assessment of long-term global ozone trends challenging. However, new studies
since AR4 provide greater understanding of surface and free tropospheric ozone trends from the 1950s
through 2010. An extensive compilation of measured ozone trends is presented in the Supplementary
Material, Figure 2.SM.1 and Table 2.SM.2.
The earliest (1876–1910) quantitative ozone observations are limited to Montsouris near Paris where ozone
averaged 11 ppb (Volz and Kley, 1988). Semi-quantitative ozone measurements from over 40 locations
around the world in the late 1800s and early 1900s range from 5-32 ppb with large uncertainty (Pavelin et
al., 1999). The low 19th century ozone values cannot be reproduced by most models (Section 8.2.3.1), and
this discrepancy is an important factor contributing to uncertainty in radiative forcing calculations (Section
8.3.3.1). Limited quantitative measurements from the 1870s-1950s indicate that surface ozone in Europe
increased by more than a factor of 2 compared to observations made at the end of the 20th century (Marenco
et al., 1994; Parrish et al., 2012).
Satellite-based tropospheric column ozone retrievals across the tropics and mid-latitudes reveal a greater
burden in the NH than in the SH (Ziemke et al., 2011). Tropospheric column ozone trend analyses are few.
An analysis by Ziemke et al. (2005) found no trend over the tropical Pacific Ocean but significant positive
trends (5-9%/decade) in the mid-latitude Pacific of both hemispheres during 1979-2003 Significant positive
trends (2-9%/decade) were found across broad regions of the tropical South Atlantic, India, southern China,
southeast Asia, Indonesia and the tropical regions downwind of China (Beig and Singh, 2007).
Long-term ozone trends at the surface and in the free troposphere (of importance for calculating radiative
forcing, Chapter 8) can only be assessed from in situ measurements at a limited number of sites, leaving
large areas such as the tropics and SH sparsely sampled (Table 2.SM.2, Figure 2.7). Nineteen predominantly
rural surface sites or regions around the globe have long-term records that stretch back to the 1970s, and in
two cases the 1950s (Lelieveld et al., 2004; Parrish et al., 2012; Oltmans et al., 2013). Thirteen of these sites
are in the NH, and 11 sites have statistically significant positive trends of 1-5 ppb/decade, corresponding to >
100% ozone increases since the 1950s and 9-55% ozone increases since the 1970s. In the SH, 3 of 6 sites
have significant trends of approximately 2 ppb/decade and 3 have insignificant trends. Free tropospheric
monitoring since the 1970s is more limited. Significant positive trends since 1971 have been observed using
ozone sondes above western Europe, Japan and coastal Antarctica (rates of increase range from 1-3
ppb/decade), but not at all levels (Oltmans et al., 2013). In addition aircraft have measured significant upper
tropospheric trends in one or more seasons above the north eastern USA, the North Atlantic Ocean, Europe,
the Middle East, northern India, southern China and Japan (Schnadt Poberaj et al., 2009). Insignificant free
tropospheric trends were found above the Mid-Atlantic USA (1971-2010) (Oltmans et al., 2013) and in the
upper troposphere above the western USA (1975-2001) (Schnadt Poberaj et al., 2009). No site or region
showed a significant negative trend.
In recent decades ozone precursor emissions have decreased in Europe and North America and increased in
Asia (Granier et al., 2011), impacting ozone production on regional and hemispheric scales (Skeie et al.,
2011). Accordingly, 1990-2010 surface ozone trends vary regionally. In Europe ozone generally increased
through much of the 1990s but since 2000 ozone has either levelled off or decreased at rural and
mountaintop sites, as well as for baseline ozone coming ashore at Mace Head, Ireland (Tarasova et al., 2009;
Logan et al., 2012; Parrish et al., 2012; Oltmans et al., 2013). In North America surface ozone has increased
in eastern and Arctic Canada, but is unchanged in central and western Canada (Oltmans et al., 2013). Surface
ozone has increased in baseline air masses coming ashore along the west coast of the USA (Parrish et al.,
2012) and at half of the rural sites in the western USA during spring (Cooper et al., 2012). In the eastern
USA surface ozone has decreased strongly in summer, is largely unchanged in spring and has increased in
winter (Lefohn et al., 2010; Cooper et al., 2012). East Asian surface ozone is generally increasing (Table
2.SM.2) and at downwind sites ozone is increasing at Mauna Loa, Hawaii but decreasing at Minami Tori
Shima in the subtropical western North Pacific (Oltmans et al., 2013) . In the SH ozone has increased at the
eight available sites, although trends are insignificant at four sites (Helmig et al., 2007; Oltmans et al., 2013).
[INSERT FIGURE 2.7 HERE]
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Figure 2.7: Annual average surface ozone concentrations from regionally representative ozone monitoring sites around
the world. a) Europe. b) Asia and North America. c) Remote sites in the Northern and Southern Hemispheres. The
station name in the legend is followed by its latitude and height. Time series include data from all times of day and
trend lines are linear regressions following the method of Parrish et al. (2012). Trend lines are fit through the full time
series at each location, except for Jungfraujoch, Zugspitze, Arosa and Hohenpeissenberg where the linear trends end in
2000 (indicated by the dashed vertical line in a)). Twelve of these 19 sites have significant positive ozone trends (i.e., a
trend of zero lies outside the 90% confidence interval); the 7 sites with non-significant trends are: Japanese MBL
(marine boundary layer), Summit (Greenland), Barrow (Alaska), Storhofdi (Iceland), Samoa (tropical South Pacific
Ocean), Cape Point (South Africa) and South Pole (Antarctica).
Due to methodological changes free tropospheric ozone observations are most reliable since the mid-1990s.
Ozone has decreased above Europe since 1998 (Logan et al., 2012) and is largely unchanged above Japan
(Oltmans et al., 2013). Otherwise the remaining regions with measurements (N. America, North Pacific
Ocean, SH) show a range of positive trends (both significant and insignificant) depending on altitude, with
no site having a negative trend at any altitude (Table 2.SM.2).
In summary, there is medium confidence from limited measurements in the late 19th through mid-20th
century that European surface ozone more than doubled by the end of the 20th century. There is
medium confidence from more widespread measurements beginning in the 1970s that surface ozone
has increased at most (non-urban) sites in the NH (1-5 ppb/decade), while there is low confidence for
ozone increases (2 ppb/decade) in the SH. Since 1990 surface ozone has likely increased in East Asia,
while surface ozone in the eastern USA and western Europe has levelled off or is decreasing. Ozone
monitoring in the free troposphere since the 1970s is very limited and indicates a weaker rate of
increase than at the surface. Satellite instruments can now quantify the present-day tropospheric
ozone burden on a near-global basis; significant tropospheric ozone column increases were observed
over extended tropical regions of southern Asia, as well as mid-latitude regions of the South and North
Pacific Ocean since 1979.
2.2.2.4
Carbon Monoxide (CO), Non Methane Volatile Organic Compounds (NMVOC) and Nitrogen
Dioxide (NO2)
Emissions of CO, NMVOC and NOx= (NO + NO2) do not have a direct effect on radiative forcing, but
affect climate indirectly as precursors to tropospheric O3 and aerosol formation, and their impacts on OH
concentrations and CH4 lifetime. NMVOC include aliphatic, aromatic oxygenated hydrocarbons (e.g.,
aldehydes, alcohols, and organic acids), and have atmospheric lifetimes ranging from hours to months.
Global coverage of NMVOC measurements is poor, except for a few compounds. Reports on trends
generally indicate declines in a range of NMVOC in urban and rural regions of North America and Europe
on the order of a few percent to more than 10%/yr. Global ethane levels reported by Simpson et al. (2012)
declined by about 21% from 1986 to 2010. Measurements of air extracted from firn suggest that NMVOC
concentrations were growing until 1980 and declined afterwards (Aydin et al., 2011; Worton et al., 2012).
Satellite retrievals of formaldehyde column abundances from 1997 to 2007 show significant positive trends
over northeastern China (4%/yr) and India (1.6%/yr), possibly related to strong increases in anthropogenic
NMVOC emissions, whereas negative trends of about -3%/yr are observed over Tokyo, Japan and the
northeast U.S. urban corridor as a result of pollution regulation (De Smedt et al., 2010).
The major sources of atmospheric CO are in situ production by oxidation of hydrocarbons (mostly CH4 and
isoprene) and direct emission resulting from incomplete combustion of biomass and fossil fuels. An analysis
of MOPITT (Measurements of Pollutants in the Troposphere) and AIRS (Atmospheric Infrared Sounder)
satellite data suggest a clear and consistent decline of CO columns for 2002–2010 over a number of polluted
regions in Europe, North America and Asia with a global trend of about –1%/yr
(Yurganov et al., 2010; Fortems-Cheiney et al., 2011; Worden et al., 2013). Analysis of satellite data using
two more instruments for recent overlapping years shows qualitatively similar decreasing trends (Worden et
al., 2013), but the magnitude of trends remains uncertain due to the presence of instrument biases. Small CO
decreases observed in the NOAA and AGAGE networks are consistent with slight declines in global
anthropogenic CO emissions over the same time (Supplementary Material 2.SM.2).
Due to its short atmospheric lifetime (~hours), NOx concentrations are highly variable in time and space.
AR4 described the potential of satellite observations of NO2 to verify and improve NOx emission inventories
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and their trends; and reported strong NO2 increases by 50% over the industrial areas of China from 1996 to
2004. An extension of this analysis reveals increases between a factor of 1.7 and 3.2 over parts of China,
while over Europe and the USA NO2 has decreased by 30 to 50% between 1996 and 2010 (Hilboll et al.,
2013).
Figure 2.8 shows the changes relative to 1996 in satellite derived tropospheric NO2 columns, with a strong
upward trend over central eastern China and an overall downward trend in Japan, Europe and the USA. NO2
reductions in the USA are very pronounced after 2004, related to differences in effectiveness of NOx
emission abatements in the USA and also to changes in atmospheric chemistry of NOx (Russell et al., 2010).
Increasingly, satellite data are used to derive trends in anthropogenic NOx emissions, with Castellanos and
Boersma (2012) reporting overall increases in global emissions, driven by Asian emission increases of up to
29%/yr (1996–2006), while moderate decreases up to 7%/yr (1996–2006) are reported for North America
and Europe.
[INSERT FIGURE 2.8 HERE]
Figure 2.8: Relative changes in tropospheric NO2 column amounts (logarithmic scale) in 7 selected world regions
dominated by high NOx emissions. Values are normalized for 1996 and derived from the GOME (Global Ozone
Monitoring Experiment) instrument from 1996 to 2002 and SCIAMACHY (Scanning Imaging Spectrometer for
Atmospheric Cartography) from 2003 to 2010 (Hilboll et al., 2013). The regions are indicated in the map inset.
In summary, satellite and surface observations of ozone precursor gases NOx, CO, and non-methane
volatile organic carbons indicate strong regional differences in trends. Most notably NO2 has likely
decreased by 30-50% in Europe and North America and increased by more than a factor of 2 in Asia
since the mid-1990s.
2.2.3
Aerosols
This section assesses trends in aerosol resulting from both anthropogenic and natural sources. The
significance of aerosol changes for global dimming and brightening is discussed in Section 2.3. Chapter 7
provides additional discussion of aerosol properties, while Chapter 8 discusses future radiative forcing and
the ice-core records that contain information on aerosol changes prior to the 1980s. Chapter 11 assesses air
quality-climate change interactions. Due to the short lifetime (days to weeks) of tropospheric aerosol, trends
have a strong regional signature. Aerosol from anthropogenic sources (i.e., fossil and biofuel burning) are
mainly confined to populated regions in the NH, whereas aerosol from natural sources, such as desert dust,
sea salt, volcanoes and the biosphere, are important in both hemispheres and likely dependent on climate and
land-use change (Carslaw et al., 2010). Owing to interannual variability, long-term trends in aerosols from
natural sources are more difficult to identify (Mahowald et al., 2010).
2.2.3.1 Aerosol Optical Depth (AOD) from Remote Sensing
AOD is a measure of the integrated columnar aerosol load and is an important parameter for evaluating
aerosol-radiation interactions. AR4 described early attempts to retrieve AOD from satellites but did not
provide estimates of temporal changes in tropospheric aerosol. Little high accuracy information on AOD
changes exists prior to 1995. Better satellite sensors and ground-based sun-photometer networks, along with
improved retrieval methods and methodological intercomparisons allow assessment of regional AOD trends
since about 1995.
AOD sun photometer measurements starting in 1986 at two stations in northern Germany corroborate the
long-term decline of AOD in Europe (Ruckstuhl et al., 2008). Ground-based, cloud-screened solar
broadband radiometer measurements provide longer time-records than spectrally selective sun-photometer
data, but are less specific for aerosol retrieval. Multi-decadal records over Japan (Kudo et al., 2011) indicate
an AOD increase until the mid-1980s, followed by an AOD decrease until the late 1990s and almost constant
AOD in the 2000s. Similar broad-band solar radiative flux multi-decadal trends have been observed for
urban-industrial regions of Europe and North America (Wild et al., 2005), and were linked to successful
measures to reduce sulphate emissions since the mid-1980s (Section 2.3). An indirect method to estimate
AOD is offered by ground-based visibility observations. These data are more ambiguous to interpret, but
records go further back in time than broadband, sun photometer and satellite data. A multi-regional analysis
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for 1973-2007 (Wang et al., 2009a) shows that prior to the 1990s visibility-derived AOD was relatively
constant in most regions analysed (except for positive trends in southern Asia), but after 1990 positive AOD
trends were observed over Asia, and parts of South America, Australia and Africa, and mostly negative AOD
trends were found over Europe. In North America, a small step-wise decrease of visibility after 1993 was
likely related to methodological changes (Wang et al., 2012f).
AOD can be most accurately determined with sun photometers that measure direct solar intensity in the
absence of cloud interferences with an absolute uncertainty of single measurements of ± 0.01% (Holben et
al., 1998). AERONET (AErosol RObotic NETwork) is a global sun photometer network (Holben et al.,
1998), with densest coverage over Europe and North America. AERONET AOD temporal trends were
examined in independent studies (de Meij et al., 2012; Hsu et al., 2012; Yoon et al., 2012), using different
data selection and statistical methods. Hsu et al. (2012) investigated AOD trends at 12 AERONET sites with
data coverage of at least ten years between 1997 and 2010. Yoon et al. (2012) investigated AOD and size
trends at 14 AERONET sites with data coverage varying between four and twelve years between 1997 and
2009. DeMeij et al. (2012) investigated AOD trends between 2000 and 2009 (550 nm; monthly data) at 62
AERONET sites mostly located in USA and Europe. Each of these studies noted an increase in AOD over
East Asia and reductions in North American and Europe. The only dense sun photometer network over
southern Asia, ARFINET (Aerosol Radiative Forcing over India NETwork), shows an increase in AOD of
about 2%/yr during the last 1-2 decades (Krishna Moorthy et al., 2013), with an absolute uncertainty of ±
0.02 at 500 nm (Krishna Moorthy et al., 2007). In contrast, negative AOD trends are identified at more than
80% of examined European and North American AERONET sites (de Meij et al., 2012). Decreasing AOD is
also observed near the west coast of northern Africa, where aerosol loads are dominated by Saharan dust
outflow. Positive AOD trends are found over the Arabian Peninsula, where aerosol is dominated by dust.
Inconsistent AOD trends reported for stations in central Africa result from the use of relatively short time
series with respect to the large interannual variability caused by wildfires and dust emissions.
Aerosol products from dedicated satellite sensors complement surface-based AOD with better spatial
coverage. The quality of the satellite-derived AOD strongly depends on the retrieval’s ability to remove
scenes contaminated by clouds and to accurately account for reflectivity at Earth’s surface. Due to relatively
weak reflectance of incoming sunlight by the sea-surface, the typical accuracy of retrieved AOD over oceans
(uncertainty of 0.03 +0.05*AOD; Kahn et al. (2007)) is usually better than over continents (uncertainty of
0.05 +0.15*AOD, Levy et al. (2010)).
Satellite based AOD trends at 550 nm over oceans from conservatively cloud-screened MODIS data (Zhang
and Reid, 2010) for 2000-2009 are presented in Figure 2.9. Strongly positive AOD trends were observed
over the oceans adjacent to southern and eastern Asia. Positive AOD trends are also observed over most
tropical oceans. The negative MODIS AOD trends observed over coastal regions of Europe and near the east
coast of the USA are in agreement with sun photometer observations and in situ measurements (Section
2.2.3.2) of aerosol mass in these regions. These regional changes over oceans are consistent with analyses of
AVHRR (Advanced Very High Resolution Radiometer) trends for 1981-2005 (Mishchenko et al., 2007;
Cermak et al., 2010; Zhao et al., 2011), except over the Southern Ocean (45°S–60°S), where negative AOD
trends of AVHRR retrievals are neither confirmed by MODIS after 2001 (Zhang and Reid, 2010) nor by
ATSR-2 (Along Track Scanning Radiometer) for 1995-2001 (Thomas et al., 2010).
[INSERT FIGURE 2.9 HERE]
Figure 2.9: a) Annual average Aerosol Optical Depth (AOD) trends at 0.55 μm for 2000–2009, based on deseasonalized, conservatively cloud-screened MODIS aerosol data over oceans (Zhang and Reid, 2010). Negative AOD
trends off Mexico are due to enhanced volcanic activity at the beginning of the record. Most non-zero trends are
significant (i.e., a trend of zero lies outside the 95% confidence interval). b) Seasonal average AOD trends at 0.55 μm
for 1998 to 2010 using SeaWiFS data (Hsu et al., 2012). Grey areas indicate incomplete or missing data. Black dots
indicate significant trends (i.e., a trend of zero lies outside the 95% confidence interval).
Satellite based AOD changes for both land and oceans (Figure 2.9b) were examined with re-processed
SeaWiFS (Sea-viewing Wide Field-of-view Sensor) AOD data for 1998-2010 (Hsu et al., 2012). A small
positive global average AOD trend is reported, which is likely influenced by interannual natural aerosol
emissions variability (e.g., related to ENSO or NAO; Box 2.5), and compensating larger positive and
negative regional AOD trends. In addition, temporal changes in aerosol composition are ignored in the
retrieval algorithms, giving more uncertain trends than suggested by statistical analysis alone (Mishchenko et
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al., 2012). Thus, confidence is low for global satellite derived AOD trends over these relatively short time
periods.
The sign and magnitude of SeaWiFS regional AOD trends over continents are in agreement with most AOD
trends by ground-based sun photometer data (see above) and with MODIS trends (Figure 2.9). The strong
positive AOD trend over the Arabian Peninsula occurs mainly during spring (MAM) and summer (JJA),
during times of dust transport, and is also visible in MODIS data (Figure 2.9). The positive AOD trend over
southern and eastern Asia is strongest during the dry seasons (DJF, MAM), when reduced wet deposition
allows anthropogenic aerosol to accumulate in the troposphere. AOD over the Saharan outflow region off
western Africa displays the strongest seasonal AOD trend differences, with AOD increases only in spring,
but strong AOD decreases during the other seasons. SeaWifs AOD decreases over Europe and the USA and
increases over southern and eastern Asia (especially during the dry season) are in agreement with reported
temporal trends in anthropogenic emissions, and surface observations (Section 2.2.3.2).
In summary, based on satellite and surface-based remote sensing it is very likely that AOD has
decreased over Europe and the eastern USA since the mid 1990s and increased over eastern and
southern Asia since 2000. In the 2000s dust-related AOD has been increasing over the Arabian
Peninsula and decreasing over the North Atlantic Ocean. Aerosol trends over other regions are less
strong or not significant during this period due to relative strong interannual variability. Overall,
confidence in satellite based global average AOD trends is low.
2.2.3.2 In Situ Surface Aerosol Measurements
AR4 did not report trends in long-term surface-based in situ measurements of particulate matter, its
components or its properties. This section summarizes reported trends of PM10, PM2.5 (Particulate Matter
with aerodynamic diameters < 10 and 2.5 μm, respectively), sulphate, and equivalent black carbon/elemental
carbon, from regionally representative measurement networks. An overview of current networks and
definitions pertinent to aerosol measurements is given in Supplementary Material 2.SM.2.3. Studies
reporting trends representative for regional changes are presented in Table 2.2. Long-term data are almost
entirely from North America and Europe, whereas a few individual studies on aerosol trends in India and
China are reported in Supplementary Material 2.SM.2.3. Figure 2.10 gives an overview of observed PM10,
PM2.5, and sulphate trends in North America and Europe for 1990–2009 and 2000–2009.
[INSERT FIGURE 2.10 HERE]
Figure 2.10: Trends in particulate matter (PM10 and PM2.5 with aerodynamic diameters < 10 and 2.5 μm, respectively)
and sulphate in Europe and USA for two overlapping periods 2000-2009 (a, b, c) and 1990-2009 (d, e). The trends are
based on measurements from the EMEP (Torseth et al., 2012) and IMPROVE (Hand et al., 2011) networks in Europe
and USA, respectively. Sites with significant trends (i.e., a trend of zero lies outside the 95% confidence interval) are
shown in colour; black dots indicate sites with non-significant trends.
In Europe, strong downward trends are observed for PM10, PM2.5 and sulphate from the rural stations in the
EMEP (European Monitoring and Evaluation Programme) network. For 2000-2009, PM2.5 shows an average
reduction of 3.9%/yr for the 6 stations with significant trends, while trends are not significant at 7 other
stations. Over 2000-2009, PM10 at 12 (out of 24) sites shows significant downward trend of on average
2.6%/yr. Similarly sulphate strongly decreased at 3.1%/yr from 1990 to 2009 with 26 of 30 sites having
significant reductions. The largest decrease occurred before 2000, while for 2000–2009, the trends were
weaker and less robust. This is consistent with reported emission reductions of 65% from 1990 to 2000 and
28% from 2001 to 2009 (Yttri et al., 2011; Torseth et al., 2012). Model analysis (Pozzoli et al., 2011)
attributed the trends in large part to emission changes.
In the USA, the largest reductions in PM and sulphate are observed in the 2000s, rather than the 1990s as in
Europe. IMPROVE (U.S. Interagency Monitoring of Protected Visual Environments Network) PM2.5
measurements (Hand et al., 2011) show significant downward trends averaging 4.0%/yr for 2000–2009 at
sites with significant trends, and 2.1%/yr at all sites, and PM10 decreases of 3.1%/yr for 2000–2009. Declines
of PM2.5 and SO42- in Canada are very similar (Hidy and Pennell, 2010) with annual mean PM2.5 at urban
measurement decreasing by 3.6%/yr during 1985-2006 (Canada, 2012).
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In the eastern and southwestern USA, IMPROVE data show strong sulphate declines, which range from 2 to
6%/yr, with average of 2.3%/yr for the sites with significant negative trends for 1990–2009. However, four
IMPROVE sites show strong SO42– increases from 2000 to 2009, amounting to 11.9%/yr, at Hawaii (1225 m
above sea level), and 4–7%/yr at 3 sites in southwest Alaska.
A recent study on long-term trends in aerosol optical properties from 24 globally distributed background
sites (Collaud Coen et al., 2013) reported statistically significant trends at 16 locations, but the sign and
magnitude of the trends varied largely with the aerosol property considered and geographical region (Table
2.3). Among the sites, this study reported strong increases in absorption and scattering coefficients in the free
troposphere at Mauna Loa, Hawaii (3400 m above sea level), which is a regional feature also evident in the
satellite-based AOD trends (illustrated in Figure 2.9). Possible explanations for these changes include the
influence of increasing Asian emissions and changes in clouds and removal processes. More and longer
Asian time series, coupled with transport analyses, are needed to corroborate these findings. Aerosol number
concentrations (Asmi et al., 2013) are declining significantly at most sites in Europe, North America, the
Pacific and the Caribbean, but increasing at South Pole based on a study of 17 globally distributed remote
sites.
Table 2.2: Trend estimates for various aerosol variables reported in the literature, using datasets with at least 10 years
of measurements. Unless otherwise noted, trends of individual stations were reported in %/yr, and 95% confidence
intervals. The standard deviation (in parentheses) is determined from the individual trends of a set of regional stations.
Trend numbers indicated by # refer to the subset of stations with significant changes over time - generally in regions
strongly influenced by anthropogenic emissions (Figure 2.10).
Aerosol
variable
Trend, %/yr
(1standard
deviation)
Period
PM2.5
-2.9 (1.31)
-3.9 (0.87)#
2000-2009
PM10
-1.9 (1.43)
-2.6 (1.19)#
2000-2009
SO42-
-3.0 (0.82)
-3.1 (0.72)#
1990-2009
SO42-
- 1.5 (1.41)
-2.0 (1.8)#
2000-2009
PM10
-1.9
1991-2008
PM2.5
-2.1 (2.08)
-4.0 (1.01)#
2000-2009
PM2.5
-1.5 (1.25)
-2.1 (0.97)#
-1.7 (2.00)
-3.1 (1.65)#
-3.0 (2.86)
-3.0 (0.62)#
1990-2009
-2.0 (1.07)
-2.3 (0.85)#
-2.5 to -7.5
1990-2009
PM10
SO42-
SO42Total
Carbon
Do Not Cite, Quote or Distribute
Reference
Europe
Adapted from
(Torseth et al.,
2012) Regional
background sites
(Barmpadimos et
al., 2012) Rural
and urban sites
USA
Adapted from
(Hand et al.,
2011)
Regional
background sites
2000-2009
2000-2009
1989-2008
(Hand et al.,
2011) Regional
background sites
2-21
Comments
13 sites available, 6 sites show statistically
significant results. Average change was 0.37 and -0.52# g/m3/yr.
24 sites available, 12 sites show statistically
significant results. Average change was 0.29 and -0.40#g/m3/yr.
30 sites available, 26 sites show statistically
significant results. Average change was 0.04 and -0.04#g/m3/yr.
30 sites available, 10 sites show statistically
significant results. Average change was 0.01 and -0.01# g/m3/yr
10 sites in Switzerland. The trend is adjusted
for change in meteorology- unadjusted data
did not differ strongly. The average change
was -0.51 g/m3/yr.
153 sites available, 52 sites show statistically
significant negative results. Only 1 site show
statisticallly positive trend
153 sites available, 39 sites show statistically
significant results.
154 sites available, 37 sites show statistically
significant results.
154 sites available, 83 sites show statistically
significant negative results. 4 sites showed
statistical positive trend.
103 sites available, 41 sites show statistically
significant results.
The trend interval includes ca 50 sites
mainly located along the east and west
coasts of the USA; fewer sites were situated
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in the central part of the continent.
EBCa
-3.8 (0.7)##
1989-2008
2SO4
-3.0 (0.6) ##
1985-2006
EBCa
Not sig. ##
1998-2008
2SO4
Not sig. ##
1997-2008
EBCa
-9.0 (5.0) ##
2002-2009
SO42-1.9 (1.7) ##
1990-2008
a
Equivalent Black Carbon.
##
Trend values significant at 1% level
Arctic
(Hirdman et al.,
2010)
Alert, Canada 62.3° W 82.5°N
Barrow, Alaska, 156.6° W 71.3° N
Zeppelin, Svalbard, 11.9° E 78.9° N
Table 2.3: Summary table of aerosol optical property trends reported in the literature, using datasets with at least 10
years of measurements. Otherwise as in Table 2.2.
Region
Trend, %/yr
(1standar
d deviation)
Period
2001-2010
Europe (4/1)
USA (14/10)
Mauna Loa (1/1)
Arctic (1/0)
Antarctica (1/0)
+0.6 (1.9)
+2.7#
-2.0 (2.5)
-2.9 (2.4)#
+2.7
+2.4
+2.5
Reference
Comments
Scattering coefficient
Adapted from
Trend study including 24 regional background sites
(Collaud Coen et
with more than 10 years of observations. Regional
al., 2013)
averages for last 10 years are included here. Values in
Regional
parenthesis show total number of sites / number of
background sites
sites with significant trend.
Absorption coefficient
Adapted from
Trend study of aerosol optical properties including 24
(Collaud Coen et
regional background sites with more than 10 years of
al., 2013)
observations. Regional averages for last 10 years are
Regional
included here. Values in parenthesis show total
background sites
number of sites and number of sites with significant
trend.
Particle number concentration
2001-2010 Adapted from
Trend study of particle number concentration (N) and
(Asmi et al.,
size distribution including 17 regional background
2013) Regional
sites. Regional averages of particle number
background sites
concentration for last 10 years are included here.
Values in parenthesis show total number of sites and
number of sites with significant trend.
2001-2010
Europe (3/0)
USA (1/1)
Mauna Loa (1/1)
Arctic (1/1)
Antarctica (1/1)
+0.3 (0.4)
-2.0
+9.0
-6.5
-0.1
Europe (4/2)
-0.9 (1.8)
-2.3 (1.0)#
-5.3 (2.8)
-6.6 (1.1)#
-3.5
-1.3
+2.7 (1.4)
North America and
Caribbean (3/3)
Mauna Loa (1/1)
Arctic (1/0)
Antarctica (2/2)
Total carbon (= light absorbing carbon + organic carbon) measurements indicate highly significant
downward trends between 2.5 and 7.5%/yr along the east and west coasts of the USA, and smaller and less
significant trends in other regions of the USA from 1989 to 2008 (Hand et al., 2011; Murphy et al., 2011).
In Europe, Torseth et al. (2012) suggest a slight reduction in elemental carbon concentrations at two stations
from 2001 to 2009, subject to large interannual variability. Collaud Coen et al. (2013) reported consistent
negative trends in the aerosol absorption coefficient at stations in the continental USA, Arctic and
Antarctica, but mostly insignificant trends in Europe over the last decade.
In the Arctic, changes in aerosol impact the atmosphere’s radiative balance as well as snow and ice albedo.
Similar to Europe and the USA, Hirdman et al. (2010) reported downward trends in equivalent black carbon
and SO42- for two out of total three Arctic stations and attributed them to emission changes.
In summary, declining AOD in Europe and North America is corroborated by very likely downward
trends in ground-based in situ particulate matter measurements since the mid-1980s. Robust evidence
from around 200 regional background sites with in situ ground based aerosol measurements indicate
downward trends in the last 2 decades of PM2.5 in parts of Europe (2–6%/yr) and the USA (1–
2.5%/yr), and also for SO42– (2-5%/yr). The strongest decreases were in the 1990s in Europe and in the
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2000s in the USA. There is robust evidence for downward trends of light absorbing aerosol in the USA
and the Arctic, while elsewhere in the world in situ time series are lacking or not long enough to reach
statistical significance.
[START BOX 2.2 HERE]
Box 2.2: Quantifying Changes in the Mean: Trend Models and Estimation
Many statistical methods exist for estimating trends in environmental time series (see Chandler and Scott,
2011 for a review). The assessment of long-term changes in historical climate data requires trend models that
are transparent and reproducible, and that can provide credible uncertainty estimates.
Linear Trends
Historical climate trends are frequently described and quantified by estimating the linear component of the
change over time (e.g., AR4). Such linear trend modelling has broad acceptance and understanding based on
its frequent and widespread use in the published research assessed in this report, and its strengths and
weaknesses are well known (von Storch and Zwiers, 1999; Wilks, 2006). Challenges exist in assessing the
uncertainty in the trend and its dependence on the assumptions about the sampling distribution (Gaussian or
otherwise), uncertainty in the data used, dependency models for the residuals about the trend line, and
treating their serial correlation (Von Storch, 1999; Santer et al., 2008).
The quantification and visualisation of temporal changes are assessed in this chapter using a linear trend
model that allows for first order autocorrelation in the residuals (Santer et al., 2008; Supplementary Material
2.SM.3). Trend slopes in such a model are the same as ordinary least squares trends; uncertainties are
computed using an approximate method. The 90% confidence interval quoted is solely that arising from
sampling uncertainty in estimating the trend. Structural uncertainties, to the extent sampled, are apparent
from the range of estimates from different datasets. Parametric and other remaining uncertainties (Box 2.1),
for which estimates are provided with some datasets, are not included in the trend estimates shown here, so
that the same method can be applied to all datasets considered.
Non-linear Trends
There is no a priori physical reason why the long-term trend in climate should be linear in time. Climatic
time series often have trends for which a straight line is not a good approximation (e.g., Seidel and Lanzante,
2004). The residuals from a linear fit in time often do not follow a simple autoregressive or moving average
process, and linear trend estimates can easily change when estimates are recalculated using data covering
shorter or longer time periods or when new data are added. When linear trends for two parts of a longer time
series are calculated separately, the trends calculated for two shorter periods may be very different (even in
sign) from the trend in the full period, if the time series exhibits significant nonlinear behavior in time (Box
2.2, Table 1).
Many methods have been applied to estimate the long-term change in a time series without assuming the
change is linear in time (e.g., Wu et al., 2007; Craigmile and Guttorp, 2011). Box 2.2, Figure 1 shows the
linear least squares and a non-linear trend fit to the GMST values from the HadCRUT4 dataset (Section
2.4.3). The non-linear trend is obtained by fitting a smoothing spline trend (Wood, 2006; Scinocca et al.,
2010) while allowing for first order autocorrelation in the residuals (Supplementary Material 2.SM.3). This
method indicates that there are significant departures from linearity in the trend estimated this way.
Box 2.2, Table 1 shows estimates of the change in the GMST from the two methods. The methods give
similar estimates with 90% confidence intervals that overlap one another. Smoothing methods that do not
assume the trend is linear can provide useful information on the structure of change that is not as well treated
with linear fits. The linear trend fit is used in this chapter because it can be applied consistently to all the
datasets, is relatively simple, transparent and easily comprehended, and is frequently used in the published
research assessed here.
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[INSERT BOX 2.2, FIGURE 1 HERE]
Box 2.2, Figure 1: a) Global Mean Surface Temperature (GMST) anomalies relative to a 1961–1990 climatology based
on HadCRUT4 annual data. The straight black lines are Least Squares trends for 1901–2012, 1901–1950 and 1951–
2012. b) Same data as in a), with Smoothing Spline (solid curve) and the 90% confidence interval on the smooth curve
(dashed lines). Note that the (strongly overlapping) 90% confidence intervals for the least square lines in (a) are omitted
for clarity. See Figure 2.20 for the other two GMST data products.
Box 2.2, Table 1: Estimates of the mean change in Global Mean Surface Temperature (GMST) between 1901 and
2012, 1901 and 1950, and 1951 and 2012, obtained from the linear (Least Squares) and non-linear (Smoothing Spline)
trend models. Half-widths of the 90% confidence intervals are also provided for the estimated changes from the two
trend methods.
Trends in °C/decade
Method
1901–2012
1901–1950
1951–2012
Least Squares
0.075 ± 0.013
0.107 ± 0.026
0.106 ± 0.027
Smoothing Spline
0.081 ± 0.010
0.070 ± 0.016
0.090 ± 0.018
[END BOX 2.2 HERE]
2.3
Changes in Radiation Budgets
The radiation budget of Earth is a central element of the climate system. On average, radiative processes
warm the surface and cool the atmosphere, which is balanced by the hydrological cycle and sensible heating.
Spatial and temporal energy imbalances due to radiation and latent heating produce the general circulation of
the atmosphere and oceans. Anthropogenic influence on climate occurs primarily through perturbations of
the components of the Earth radiation budget.
The radiation budget at the Top Of Atmosphere (TOA) includes the absorption of solar radiation by Earth,
determined as the difference between the incident and reflected solar radiation at the TOA, as well as the
thermal outgoing radiation emitted to space. The surface radiation budget takes into account the solar fluxes
absorbed at Earth’s surface, as well as the upward and downward thermal radiative fluxes emitted by the
surface and atmosphere, respectively. In view of new observational evidence since AR4, the mean state as
well as multi-decadal changes of the surface and TOA radiation budgets are assessed in the following.
2.3.1
Global Mean Radiation Budget
Since AR4, knowledge on the magnitude of the radiative energy fluxes in the climate system has improved,
requiring an update of the global annual mean energy balance diagram (Figure 2.11). Energy exchanges
between Sun, Earth and Space are observed from space-borne platforms such as the Clouds and the Earth’s
Radiant Energy System (CERES, Wielicki et al., 1996) and the Solar Radiation and Climate Experiment
(SORCE, Kopp and Lawrence, 2005) which began data collection in 2000 and 2003, respectively. The total
solar irradiance (TSI) incident at the TOA is now much better known, with the SORCE Total Irradiance
Monitor (TIM) instrument reporting uncertainties as low as 0.035%, compared to 0.1% for other TSI
instruments (Kopp et al., 2005). During the 2008 solar minimum, SORCE/TIM observed a solar irradiance
of 1360.8 ± 0.5 W/m2 compared to 1365.5 ± 1.3 W/m2 for instruments launched prior to SORCE and still
operating in 2008 (Section 8.2.1). Kopp and Lean (2011) conclude that the SORCE/TIM value of TSI is the
most credible value because it is validated by a National Institute of Standards and Technology calibrated
cryogenic radiometer. This revised TSI estimate corresponds to a solar irradiance close to 340 W/m2 globally
averaged over Earth’s sphere (Figure 2.11).
[INSERT FIGURE 2.11 HERE]
Figure 2.11: Global mean energy budget under present day climate conditions. Numbers state magnitudes of the
individual energy fluxes in W/m2, adjusted within their uncertainty ranges to close the energy budgets. Numbers in
parentheses attached to the energy fluxes cover the range of values in line with observational constraints. Figure
adapted from Wild et al. (2013).
The estimate for the reflected solar radiation at the TOA in Figure 2.11, 100 W/m2, is a rounded value based
on the CERES Energy Balanced and Filled (EBAF) satellite data product (Loeb et al., 2009; Loeb et al.,
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2012b) for the period 2001–2010. This dataset adjusts the solar and thermal TOA fluxes within their range of
uncertainty to be consistent with independent estimates of the global heating rate based upon in situ ocean
observations (Loeb et al., 2012b). This leaves 240 W/m2 of solar radiation absorbed by Earth, which is
nearly balanced by thermal emission to space of about 239 W/m2 (based on CERES EBAF), considering a
global heat storage of 0.6 W/m2 (imbalance term in Figure 2.11) based on Argo data from 2005 to 2010
(Hansen et al., 2011; Loeb et al., 2012b; Box 3.1). The stated uncertainty in the solar reflected TOA fluxes
from CERES due to uncertainty in absolute calibration alone is ~2% (2-sigma), or equivalently 2 W/m2
(Loeb et al., 2009). The uncertainty of the outgoing thermal flux at the TOA as measured by CERES due to
calibration is ~3.7 W/m2 (2σ). In addition to this, there is uncertainty in removing the influence of instrument
spectral response on measured radiance, in radiance-to-flux conversion, and in time-space averaging, which
adds up to another 1 W/m2 (Loeb et al., 2009).
The components of the radiation budget at the surface are generally more uncertain than their counterparts at
the TOA because they cannot be directly measured by passive satellite sensors and surface measurements are
not always regionally or globally representative. Since AR4, new estimates for the downward thermal
infrared radiation at the surface have been established that incorporate critical information on cloud base
heights from space-borne radar and lidar instruments (L'Ecuyer et al., 2008; Stephens et al., 2012a; Kato et
al., in press). In line with studies based on direct surface radiation measurements (Wild et al., 1998; Wild et
al., 2013) these studies propose higher values of global mean downward thermal radiation than presented in
previous IPCC assessments and typically found in climate models, exceeding 340 W/m2 (Figure 2.11). This
aligns with the downward thermal radiation in the ERA-Interim and ERA-40 reanalyses (Box 2.3), of 341
and 344 W/m2, respectively (Berrisford et al., 2011). Estimates of global mean downward thermal radiation
computed as a residual of the other terms of the surface energy budget (Kiehl and Trenberth, 1997; Trenberth
et al., 2009) are lower (324−333 W/m2), highlighting remaining uncertainties in estimates of both radiative
and non-radiative components of the surface energy budget.
Estimates of absorbed solar radiation at Earth’s surface include considerable uncertainty. Published global
mean values inferred from satellite retrievals, reanalyses and climate models range from below 160 W/m2 to
above 170 W/m2. Recent studies taking into account surface observations as well as updated spectroscopic
parameters and continuum absorption for water vapor favour values towards the lower bound of this range,
near 160 W/m2, and an atmospheric solar absorption around 80 W/m2 (Figure 2.11) (Kim and Ramanathan,
2008; Trenberth et al., 2009; Kim and Ramanathan, 2012; Trenberth and Fasullo, 2012b; Wild et al., 2013).
The ERA-Interim and ERA-40 reanalyses further support an atmospheric solar absorption of this magnitude
(Berrisford et al., 2011). Latest satellite-derived estimates constrained by CERES now also come close to
these values (Kato et al., in press). Recent independently derived surface radiation estimates favour therefore
a global mean surface absorbed solar flux near 160 W/m2 and a downward thermal flux slightly above 340
W/m2, respectively (Figure 2.11).
The global mean latent heat flux is required to exceed 80 W/m2 to close the surface energy balance in Figure
2.11, and comes close to the 85 W/m2 considered as upper limit by Trenberth and Fasullo (2012b) in view of
current uncertainties in precipitation retrieval in the Global Precipitation Climatology Project (GPCP, Adler
et al., 2012) (the latent heat flux corresponds to the energy equivalent of evaporation, which globally equals
precipitation; thus its magnitude may be constrained by global precipitation estimates). This upper limit has
recently been challenged by Stephens et al. (2012b). The emerging debate reflects potential remaining
deficiencies in the quantification of the radiative and non-radiative energy balance components and
associated uncertainty ranges, as well as in the consistent representation of the global mean energy and water
budgets (Stephens et al., 2012b; Trenberth and Fasullo, 2012b; Wild et al., 2013). Relative uncertainty in the
globally averaged sensible heat flux estimate remains high due to the very limited direct observational
constraints (Trenberth et al., 2009; Stephens et al., 2012b).
In summary, newly available observations from both space-borne and surface-based platforms allow a
better quantification of the Global Energy Budget, even though notable uncertainties remain,
particularly in the estimation of the non-radiative surface energy balance components.
2.3.2
Changes in Top of Atmosphere Radiation Budget
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While the previous section emphasized the temporally-averaged state of the radiation budget, the focus in the
following is on the temporal (multi-decadal) changes of its components. Variations in TSI are discussed in
Section 8.4.1. AR4 reported large changes in tropical TOA radiation between the 1980s and 1990s based
upon observations from the Earth Radiation Budget Satellite (ERBS) (Wielicki et al., 2002; Wong et al.,
2006). While the robust nature of the large decadal changes in tropical radiation remains to be established,
several studies have suggested links to changes in atmospheric circulation (Allan and Slingo, 2002; Chen et
al., 2002; Clement and Soden, 2005; Merrifield, 2011) (Section 2.7).
Since AR4, CERES enabled the extension of satellite records of TOA fluxes into the 2000s (Loeb et al.,
2012b). The extended records from CERES suggest no noticeable trends in either the tropical or global
radiation budget during the first decade of the 21st century (e.g., Andronova et al., 2009; Harries and Belotti,
2010; Loeb et al., 2012a; Loeb et al., 2012b). Comparisons between ERBS/CERES thermal radiation and
that derived from the NOAA High Resolution Infrared Radiation Sounder (HIRS) (Lee et al., 2007) show
good agreement until approximately 1998, corroborating the rise of 0.7 W/m2 between the 1980s and 1990s
reported in AR4. Thereafter the HIRS thermal fluxes show much higher values, likely due to changes in the
channels used for HIRS/3 instruments launched after October 1998 compared to earlier HIRS instruments
(Lee et al., 2007).
On a global scale, interannual variations in net TOA radiation and ocean heating rate (OHR) should
correspond, since oceans have a much larger effective heat capacity than land and atmosphere, and therefore
serve as the main reservoir for heat added to the Earth-atmosphere system (Box 3.1). Wong et al. (2006)
showed that interannual variations in these two data sources are in good agreement for 1992–2003. In the
ensuing 5 years, however, Trenberth and Fasullo (2010) note that the two diverge with ocean in situ
measurements (Levitus et al., 2009) indicating a decline in OHR in contrast to expectations from the
observed net TOA radiation. The divergence after 2004 is referred to as ‘‘missing energy’’ by Trenberth and
Fasullo (2012b), who further argue that the main sink of the missing energy likely occurs at ocean depths
below 275 meters. Loeb et al. (2012b) compared interannual variations in CERES net radiation with OHRs
derived from three independent ocean heat content anomaly analyses and included an error analysis of both
CERES and the OHRs. They conclude that the apparent decline in OHR is not statistically robust and that
differences between interannual variations in OHR and satellite net TOA flux are within the uncertainty of
the measurements (Figure 2.12). They further note that between January 2001 and December 2012, Earth has
been steadily accumulating energy at a rate of 0.50 ± 0.43 W/m2 (90% CI). Hansen et al. (2011) obtained a
similar value for 2005-2010 using an independent analysis of the ocean heat content anomaly data (von
Schuckmann and Le Traon, 2011). The variability in Earth’s energy imbalance is strongly influenced by
ocean circulation changes relating to the ENSO (Box 2.5); during cooler La Niña years (e.g., 2009) less
thermal radiation is emitted and the climate system gains heat while the reverse is true for warmer El Niño
years (e.g., 2010) (Figure 2.12).
In summary, satellite records of TOA radiation fluxes have been substantially extended since AR4. It
is unlikely that significant trends exist in global and tropical radiation budgets since 2000. Interannual
variability in the Earth’s energy imbalance related to ENSO is consistent with ocean heat content
records within observational uncertainty.
[INSERT FIGURE 2.12 HERE]
Figure 2.12: Comparison of net Top Of Atmosphere (TOA) flux and upper Ocean Heating Rates (OHR). Global annual
average (July to June) net TOA flux from CERES observations (based upon the EBAF-TOA_Ed2.6r product)(black
line) and 0-700 (blue) and 0-1,800 m (red) OHR from the Pacific Marine Environmental Laboratory/Jet Propulsion
Laboratory/Joint Institute for Marine and Atmospheric Research (PMEL/JPL/JIMAR), 0-700 m OHR from the National
Oceanic Data Center (NODC) (Levitus et al., 2009)(green), and 0-700 m OHR from the Hadley Center (Palmer et al.,
2007)(brown). The length of the coloured bars corresponds to the magnitude of OHR. Thin vertical lines are error bars,
corresponding to the magnitude of uncertainties. Uncertainties for all annual OHR are given at one standard error
derived from ocean heat content anomaly uncertainties (Lyman et al., 2010). CERES net TOA flux uncertainties are
given at the 90% confidence level determined following Loeb et al. (2012b). Adapted from Loeb et al.(2012b).
2.3.3
2.3.3.1
Changes in Surface Radiation Budget
Surface Solar Radiation
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Changes in radiative fluxes at the surface can be traced further back in time than the satellite-based TOA
fluxes, although only at selected terrestrial locations where long-term records exist. Monitoring of radiative
fluxes from land-based stations began on a widespread basis in the mid-20th century, predominantly
measuring the downward solar component, also known as global radiation or surface solar radiation (SSR).
AR4 reported on the first indications for substantial decadal changes in observational records of SSR.
Specifically, a decline of SSR from the beginning of widespread measurements in the 1950s until the mid1980s has been observed at many land-based sites (popularly known as 'global dimming', Stanhill and
Cohen, 2001; Liepert, 2002), as well as a partial recovery from the 1980s onward ('brightening', Wild et al.,
2005) (see the longest available SSR series of Stockholm, Sweden, in Figure 2.13 as an illustrative example).
Since AR4, numerous studies have substantiated the findings of significant decadal SSR changes observed
both at worldwide distributed terrestrial sites (Dutton et al., 2006; Wild et al., 2008; Gilgen et al., 2009;
Ohmura, 2009; Wild, 2009 and references therein) as well as in specific regions. In Europe, Norris and Wild
(2007) noted a dimming between 1971 and 1986 of 2.0–3.1 W/m2 per decade and subsequent brightening of
1.1–1.4 W/m2 per decade from 1987 to 2002 in a pan-European time series comprising 75 sites. Similar
tendencies were found at sites in northern Europe (Stjern et al., 2009), Estonia (Russak, 2009) and Moscow
(Abakumova et al., 2008). Chiacchio and Wild (2010) pointed out that dimming and subsequent brightening
in Europe is mainly seen in spring and summer. Brightening in Europe from the 1980s onward was further
documented at sites in Switzerland, Germany, France, the Benelux, Greece and the Iberian Peninsula
(Ruckstuhl et al., 2008; Wild et al., 2009; Zerefos et al., 2009; Sanchez-Lorenzo et al., 2013). Concurrent
brightening of 2-8 W/m2 per decade was also noted at continental sites in the USA (Long et al., 2009;
Riihimaki et al., 2009; Augustine and Dutton, 2013). The general pattern of dimming and consecutive
brightening was further found at numerous sites in Japan (Norris and Wild, 2009; Ohmura, 2009; Kudo et
al., 2011) and in the SH in New Zealand (Liley, 2009). Analyses of observations from sites in China
confirmed strong declines in SSR from the 1960s to 1980s on the order of 2–8 W/m2 per decade, which also
did not persist in the 1990s (Che et al., 2005; Liang and Xia, 2005; Qian et al., 2006; Shi et al., 2008; Norris
and Wild, 2009; Xia, 2010a). On the other hand, persistent dimming since the mid-20th century with no
evidence for a trend reversal was noted at sites in India (Wild et al., 2005; Kumari et al., 2007; Kumari and
Goswami, 2010; Soni et al., 2012), and in the Canadian Prairie (Cutforth and Judiesch, 2007). Updates on
latest SSR changes observed since 2000 provide a less coherent picture (Wild, 2012). They suggest a
continuation of brightening at sites in Europe, U.S., and parts of Asia, a levelling off at sites in Japan and
Antarctica, and indications for a renewed dimming in parts of China (Wild et al., 2009; Xia, 2010a).
The longest observational SSR records, extending back to the 1920s and 1930s at a few sites in Europe,
further indicate some brightening during the first half of the 20th century, known as ‘early brightening’ (cf.
Figure 2.13) (Ohmura, 2009; Wild, 2009). This suggests that the decline in SSR, at least in Europe, was
confined to a period between the 1950s and 1980s.
A number of issues remain, such as the quality and representativeness of some of the SSR data as well as the
large scale significance of the phenomenon (Wild, 2012). The historic radiation records are of variable
quality and rigorous quality control is necessary to avoid spurious trends (Dutton et al., 2006; Shi et al.,
2008; Gilgen et al., 2009; Tang et al., 2011; Wang et al., 2012e; Sanchez-Lorenzo et al., 2013). Since the
mid-1990s, high-quality data are becoming increasingly available from new sites of the Baseline Surface
Radiation Network (BSRN) and Atmospheric Radiation Measurement (ARM) Program, which allow the
determination of SSR variations with unprecedented accuracy (Ohmura et al., 1998). Alpert et al. (2005) and
Alpert and Kishcha (2008) argued that the observed SSR decline between 1960 and 1990 was larger in
densely populated than in rural areas. The magnitude of this ‘urbanization effect’ in the radiation data is not
yet well quantified. Dimming and brightening is, however, also notable at remote and rural sites (Dutton et
al., 2006; Karnieli et al., 2009; Liley, 2009; Russak, 2009; Wild, 2009; Wang et al., 2012d).
Globally complete satellite estimates are available since the early 1980s (Hatzianastassiou et al., 2005;
Pinker et al., 2005; Hinkelman et al., 2009). Since satellites do not directly measure the surface fluxes, they
have to be inferred from measurable top-of-atmosphere signals using empirical or physical models to remove
atmospheric perturbations. Available satellite-derived products qualitatively agree on a brightening from the
mid-1980s to 2000 averaged globally as well as over oceans, on the order of 2–3 W m–2 per decade
(Hatzianastassiou et al., 2005; Pinker et al., 2005; Hinkelman et al., 2009). Averaged over land, however,
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trends are positive or negative depending on the respective satellite product (Wild, 2009). Knowledge of the
decadal variation of aerosol burdens and optical properties, required in satellite retrievals of SSR and
considered relevant for dimming/brightening particularly over land, is very limited (Section 2.2.3).
Extensions of satellite-derived SSR beyond 2000 indicate tendencies towards a renewed dimming at the
beginning of the new millennium (Hinkelman et al., 2009; Hatzianastassiou et al., 2012).
Reconstructions of SSR changes from more widely measured meteorological variables can help to increase
their spatial and temporal coverage. Multi-decadal SSR changes have been related to observed changes in
sunshine duration, atmospheric visibility, diurnal temperature range (DTR; Section 2.4.1.2) and pan
evaporation (Section 2.5.4). Overall, these proxies provide independent evidence for the existence of largescale multi-decadal variations in SSR. Specifically, widespread observations of declines in pan evaporation
from the 1950s to the 1980s were related to SSR dimming amongst other factors (Roderick and Farquhar,
2002). The observed decline in DTR over global land surfaces from the 1950s to the 1980s (Section 2.4.1.2),
and its stabilisation thereafter fits to a large-scale dimming and subsequent brightening, respectively (Wild et
al., 2007). Widespread brightening after 1980 is further supported by reconstructions from sunshine duration
records (Wang et al., 2012e). Over Europe, SSR dimming and subsequent brightening is consistent with
concurrent declines and increases in sunshine duration (Sanchez-Lorenzo et al., 2008), evaporation in energy
limited environments (Teuling et al., 2009), visibility records (Vautard et al., 2009; Wang et al., 2009b) and
DTR (Makowski et al., 2009). The early brightening in the 1930s and 1940s seen in a few European SSR
records is in line with corresponding changes in sunshine duration and DTR (Sanchez-Lorenzo et al., 2008;
Wild, 2009; Sanchez-Lorenzo and Wild, 2012). In China, the levelling off in SSR in the 1990s after decades
of decline coincides with similar tendencies in the pan evaporation records, sunshine duration and DTR (Liu
et al., 2004a; Liu et al., 2004b; Qian et al., 2006; Ding et al., 2007; Wang et al., 2012d). Dimming up to the
1980s and subsequent brightening is also indicated in a set of 237 sunshine duration records in South
America (Raichijk, 2011).
[INSERT FIGURE 2.13 HERE]
Figure 2.13: Annual mean Surface Solar Radiation (SSR) as observed at Stockholm, Sweden, from 1923 to 2010.
Stockholm has the longest SSR record available worldwide. Updated from Wild (2009) and Ohmura (2009).
2.3.3.2
Surface Thermal and Net Radiation
Thermal radiation, also known as longwave, terrestrial or far-infrared radiation is sensitive to changes in
atmospheric greenhouse gases, temperature and humidity. Long-term measurements of the thermal surface
components as well as surface net radiation are available at far fewer sites than SSR. Downward thermal
radiation observations started to become available during the early 1990s at a limited number of globally
distributed terrestrial sites. From these records, Wild et al. (2008) determined an overall increase of 2.6 W m–
2
per decade over the 1990s, in line with model projections and the expectations of an increasing greenhouse
effect. Wang and Liang (2009) inferred an increase in downward thermal radiation of 2.2 W m–2 per decade
over the period 1973–2008 from globally available terrestrial observations of temperature, humidity and
cloud fraction. Prata (2008) estimated a slightly lower increase of 1.7 W m–2 per decade for clear sky
conditions over the earlier period 1964–1990, based on observed temperature and humidity profiles from
globally distributed land-based radiosonde stations and radiative transfer calculations. Philipona et al. (2004;
Philipona et al., 2005) and Wacker et al. (2011) noted increasing downward thermal fluxes recorded in the
Swiss Alpine Surface Radiation Budget (ASRB) network since the mid-1990s, corroborating an increasing
greenhouse effect. For mainland Europe, Philipona et al. (2009) estimated an increase of downward thermal
radiation of 2.4–2.7 W m–2 per decade for the period 1981–2005.
There is limited observational information on changes in surface net radiation, in large part because
measurements of upward fluxes at the surface are made at only a few sites and are not spatially
representative. Wild et al. (2004; 2008) inferred a decline in land surface net radiation on the order of 2
W/m2 per decade from the 1960s to the 1980s, and an increase at a similar rate from the 1980s to 2000, based
on estimated changes of the individual radiative components that constitute the surface net radiation.
Philipona et al. (2009) estimated an increase in surface net radiation of 1.3–2 W m–2 per decade for central
Europe and the Alps between 1981 and 2005.
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Implications from Observed Changes in Related Climate Elements
The observed multi-decadal SSR variations cannot be explained by changes in TSI, which are an order of
magnitude smaller (Willson and Mordvinov, 2003). They therefore have to originate from alterations in the
transparency of the atmosphere, which depends on the presence of clouds, aerosols and radiatively active
gases (Kvalevag and Myhre, 2007; Kim and Ramanathan, 2008). Cloud cover changes (Section 2.5.7)
effectively modulate SSR on an interannual basis, but their contribution to the longer-term SSR trends is
ambiguous. While cloud cover changes were found to explain the trends in some areas (e.g., Liley, 2009),
this is not always the case, particularly in relatively polluted regions (Qian et al., 2006; Norris and Wild,
2007, 2009; Wild, 2009; Kudo et al., 2012). SSR dimming and brightening has also been observed under
cloudless atmospheres at various locations, pointing to a prominent role of atmospheric aerosols (Wild et al.,
2005; Qian et al., 2007; Ruckstuhl et al., 2008; Sanchez-Lorenzo et al., 2009; Wang et al., 2009b; Zerefos et
al., 2009).
Aerosols can directly attenuate SSR by scattering and absorbing solar radiation, or indirectly, through their
ability to act as cloud condensation nuclei, thereby changing cloud reflectivity and lifetime (Chapter 7). SSR
dimming and brightening is often reconcilable with trends in anthropogenic emission histories and
atmospheric aerosol loadings (Stern, 2006; Streets et al., 2006; Mishchenko et al., 2007; Ruckstuhl et al.,
2008; Ohvril et al., 2009; Russak, 2009; Streets et al., 2009; Cermak et al., 2010; Wild, 2012). Recent trends
in aerosol optical depth derived from satellites indicate a decline in Europe since 2000 (Section 2.2.3), in line
with evidence from SSR observations. However, direct aerosol effects alone may not be able to account for
the full extent of the observed SSR changes in remote regions with low pollution levels (Dutton and
Bodhaine, 2001; Schwartz, 2005). Aerosol indirect effects have not yet been well quantified, but have the
potential to amplify aerosol-induced SSR trends, particularly in relatively pristine environments, such as
over oceans (Wild, 2012).
SSR trends are also qualitatively in line with observed multi-decadal surface warming trends (Chapter 10),
with generally smaller warming rates during phases of declining SSR, and larger warming rates in phases of
increasing SSR (Wild et al., 2007). This is seen more pronounced for the relatively polluted NH than the
more pristine SH (Wild, 2012). For Europe, Vautard et al. (2009) found that a decline in the frequency of
low-visibility conditions such as fog, mist and haze over the past 30 years and associated SSR increase may
be responsible for 10–20% of Europe’s recent daytime warming, and 50% of eastern European warming.
Philipona (2012) noted that both warming and brightening are weaker in the European Alps compared to the
surrounding lowlands with stronger aerosol declines since 1981.
Reanalyses and observationally-based methods have been used to show that increased atmospheric moisture
with warming (Willett et al., 2008 , Section 2.5) enhances thermal radiative emission of the atmosphere to
the surface leading to reduced net thermal cooling of the surface (Prata, 2008; Allan, 2009; Philipona et al.,
2009; Wang and Liang, 2009).
In summary, the evidence for widespread multi-decadal variations in solar radiation incident on land surfaces
has been substantiated since AR4, with many of the observational records showing a decline from the 1950s
to the 1980s (‘dimming’), and a partial recovery thereafter (‘brightening’). Confidence in these changes is
high in regions with high station densities such as over Europe and parts of Asia. These likely changes are
generally supported by observed changes in related, but more widely measured variables, such as sunshine
duration, DTR and hydrological quantities, and are often in line with aerosol emission patterns. Over some
remote land areas and over the oceans, confidence is low due to the lack of direct observations, which
hamper a truly global assessment. Satellite-derived SSR fluxes support the existence of brightening also over
oceans, but are less consistent over land surface where direct aerosol effects become more important. There
are also indications for increasing downward thermal and net radiation at terrestrial stations since the early
1990s with medium confidence.
[START BOX 2.3 HERE]
Box 2.3: Global Atmospheric Reanalyses
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Dynamical reanalyses are increasingly used for assessing weather and climate phenomena. Given their more
frequent use in this assessment compared to AR4, their characteristics are described in more detail here.
Reanalyses are distinct from, but complement, more ‘traditional’ statistical approaches to assessing the raw
observations. They aim to produce continuous reconstructions of past atmospheric states that are consistent
with all observations as well as with atmospheric physics as represented in a numerical weather prediction
model, a process termed data assimilation. Unlike real-world observations, reanalyses are uniform in space
and time and provide non-observable variables (e.g., potential vorticity).
Several groups are actively pursuing reanalysis development at the global scale and many of these have
produced several generations of reanalyses products (Box 2.3, Table 1). Since the first generation of
reanalyses produced in the 1990s, substantial development has taken place. The MERRA and ERA-Interim
reanalyses show improved tropical precipitation and hence better represent the global hydrological cycle
(Dee et al., 2011b). The NCEP/CFSR reanalysis uses a coupled ocean-atmosphere-land-sea-ice model (Saha
et al., 2010). The 20th century Reanalyses (20CR, Compo et al., 2011) is a 56 member ensemble and covers
140 years by assimilating only surface and Sea Level Pressure (SLP) information. This variety of groups and
approaches provides some indication of the robustness of reanalyses when compared. In addition to the
global reanalyses, several regional reanalyses exist or are currently being produced.
Box 2.3, Table 1: Overview of global dynamical reanalysis datasets (ranked by start year; the period extends to present
if no end year is provided). A further description of reanalyses and their technical derivation is given in pp. S33-35 of
Blunden et al. (2011). Approximate resolution is calculated as 1000 km * 20/N (with N denoting the spectral truncation,
Laprise, 1992).
Institution
Reanalysis
Period
Approximate Reference
Resolution at
Equator
Cooperative Institute for Research in Environmental 20th century
1871–2010 320 km
Compo et al. (2011)
Sciences (CIRES), National Oceanic and
Reanalysis,
Atmospheric Administration (NOAA), USA
Vers. 2 (20CR)
National Centers for Environmental Prediction
NCEP/NCAR 1948–
320 km
Kistler et al. (2001)
(NCEP) and National Center for Atmospheric
R1 (NNR)
Research (NCAR), USA
European Centre for Medium-Range Weather
ERA-40
1957–2002 125 km
Uppala et al. (2005)
Forecasts (ECMWF)
Japan Meteorological Agency (JMA)
JRA-55
1958–
60 km
Ebita et al. (2011)
National Centers for Environmental Prediction
NCEP/DOE R2 1979–
320 km
Kanamitsu et al. (2002)
(NCEP), US Department of Energy, USA
Japan Meteorological Agency (JMA)
JRA-25
1979–
190 km
Onogi et al. (2007)
National Aeronautics and Space Administration
MERRA
1979–
75 km
Rienecker et al. (2011)
(NASA), USA
European Centre for Medium-Range Weather
ERA-Interim 1979–
80 km
Dee et al. (2011b)
Forecasts (ECMWF)
National Centers for Environmental Prediction
CFSR
1979–
50 km
Saha et al. (2010)
(NCEP), USA
Reanalyses products provide invaluable information on time scales ranging from daily to interannual
variability. However, they may often be unable to characterize long-term trends (Trenberth et al., 2011).
Although reanalyses projects by definition use a ‘frozen’ assimilation system, there are many other sources
of potential errors. In addition to model biases, changes in the observational systems (e.g., coverage,
introduction of satellite data) and time-dependent errors in the underlying observations or in the boundary
conditions lead to step changes in time, even in latest generation reanalyses (Bosilovich et al., 2011).
Errors of this sort were ubiquitous in early generation reanalyses and rendered them of limited value for
trend characterization (Thorne and Vose, 2010). Subsequent products have improved and uncertainties are
better understood (Dee et al., 2011a), but artefacts are still present. As a consequence, trend adequacy
depends upon the variable under consideration, the time period and the region of interest. For example,
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surface air temperature and humidity trends over land in the ERA-Interim reanalysis compare well with
observations (Simmons et al., 2010), but polar tropospheric temperature trends in ERA-40 disagree with
trends derived from radiosonde and satellite observations (Bitz and Fu, 2008; Grant et al., 2008; Graversen et
al., 2008; Thorne, 2008; Screen and Simmonds, 2011) due to problems that were resolved in ERA-Interim
(Dee et al., 2011a).
Studies based on reanalyses are used cautiously in AR5 and known inadequacies are pointed out and
referenced. Later generation reanalyses are preferred where possible, however, literature based on these new
products is still sparse.
[END BOX 2.3 HERE]
2.4
Changes in Temperature
2.4.1
2.4.1.1
Land-Surface Air Temperature
Large-Scale Records and their Uncertainties
AR4 concluded global Land-Surface Air Temperature (LSAT) had increased over the instrumental period of
record with the warming rate approximately double that reported over the oceans since 1979. Since AR4,
substantial developments have occurred including the production of revised datasets, more digital data
records, and new dataset efforts. These innovations have improved understanding of data issues and
uncertainties, allowing better quantification of regional changes. This reinforces confidence in the reported
globally averaged LSAT time series behaviour.
Global Historical Climatology Network Version 3 (GHCNv3) incorporates many improvements (Lawrimore
et al., 2011) but was found to be virtually indistinguishable at the global mean from version 2 (used in AR4).
Goddard Institute of Space Studies (GISS) continues to provide an estimate based upon primarily GHCN,
accounting for urban impacts through nightlights adjustments (Hansen et al., 2010). CRUTEM4 (Jones et al.,
2012) incorporates additional station series and also newly homogenized versions of many individual station
records. A new data product from a group based predominantly at Berkeley (Rohde et al., 2013) uses a
method that is substantially distinct from earlier efforts (further details on all the datasets and data
availability are given in Supplementary Material 2.SM.4). Despite the range of approaches, the long-term
variations and trends broadly agree among these various LSAT estimates, particularly after 1900. Global
LSAT has increased (Figure 2.14, Table 2.4).
[INSERT FIGURE 2.14 HERE]
Figure 2.14: Global annual average Land-Surface Air Temperature (LSAT) anomalies relative to a 1961–1990
climatology from the latest versions of four different datasets (Berkeley, CRUTEM, GHCN and GISS).
Table 2.4: Trend estimates and 90% confidence intervals (Box 2.2) for LSAT global average values over five common
periods.
Trends in °C per decade
Dataset
1880–2012
1901–2012
1901–1950
1951–2012
1979–2012
CRUTEM4.1.1.0 (Jones et al., 2012)
0.086 ± 0.015 0.095 ± 0.020 0.097 ± 0.029 0.175 ± 0.037 0.254 ± 0.050
GHCNv3.2.0 (Lawrimore et al., 2011) 0.094 ± 0.016 0.107 ± 0.020 0.100 ± 0.033 0.197 ± 0.031 0.273 ± 0.047
GISS (Hansen et al., 2010)
0.095 ± 0.015 0.099 ± 0.020 0.098 ± 0.032 0.188 ± 0.032 0.267 ± 0.054
Berkeley (Rohde et al., 2013)
0.094 ± 0.013 0.101 ± 0.017 0.111 ± 0.034 0.175 ± 0.029 0.254 ± 0.049
Since AR4, various theoretical challenges have been raised over the verity of global LSAT records (Pielke et
al., 2007). Globally, sampling and methodological independence has been assessed through sub-sampling
(Parker et al., 2009; Jones et al., 2012), creation of an entirely new and structurally distinct product (Rohde
et al., 2013) and a complete reprocessing of GHCN (Lawrimore et al., 2011). None of these yielded more
than minor perturbations to the global LSAT records since 1900. Willett et al. (2008) and Peterson et al.
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(2011) explicitly showed that changes in specific and relative humidity (Section 2.5.5) were physically
consistent with reported temperature trends, a result replicated in the ERA reanalyses (Simmons et al., 2010).
Various investigators (Onogi et al., 2007; Simmons et al., 2010; Parker, 2011; Vose et al., 2012a) showed
that LSAT estimates from modern reanalyses were in quantitative agreement with observed products.
Particular controversy since AR4 has surrounded the LSAT record over the United States, focussed upon
siting quality of stations in the US Historical Climatology Network (USHCN) and implications for long-term
trends. Most sites exhibit poor current siting as assessed against official WMO siting guidance, and may be
expected to suffer potentially large siting-induced absolute biases (Fall et al., 2011). However, overall biases
for the network since the 1980s are likely dominated by instrument type (since replacement of Stevenson
screens with maximum minimum temperature systems (MMTS) in the 1980s at the majority of sites), rather
than siting biases (Menne et al., 2010; Williams et al., 2012). A new automated homogeneity assessment
approach (also used in GHCNv3, Menne and Williams, 2009) was developed that has been shown to perform
as well or better than other contemporary approaches (Venema et al., 2012). This homogenization procedure
likely removes much of the bias related to the network-wide changes in the 1980s (Menne et al., 2010; Fall et
al., 2011; Williams et al., 2012). Williams et al. (2012) produced an ensemble of dataset realisations using
perturbed settings of this procedure and concluded through assessment against plausible test cases that there
existed a propensity to under-estimate adjustments. This propensity is critically dependent upon the
(unknown) nature of the inhomogeneities in the raw data records. Their homogenization increases both
minimum temperature and maximum temperature centennial-timescale United States average LSAT trends.
Since 1979 these adjusted data agree with a range of reanalysis products whereas the raw records do not (Fall
et al., 2010; Vose et al., 2012a).
Regional analyses of LSAT have not been limited to the United States. Various national and regional studies
have undertaken assessments for Europe (Winkler, 2009; Bohm et al., 2010; Tietavainen et al., 2010; van der
Schrier et al., 2011), China (Li et al., 2009; Zhen and Zhong-Wei, 2009; Li et al., 2010a; Tang et al., 2010),
India (Jain and Kumar, 2012), Australia (Trewin, 2012), Canada (Vincent et al., 2012), South America,
(Falvey and Garreaud, 2009) and East Africa (Christy et al., 2009). These analyses have used a range of
methodologies and, in many cases, more data and metadata than available to the global analyses. Despite the
range of analysis techniques they are generally in broad agreement with the global products in characterizing
the long-term changes in mean temperatures. This includes some regions, such as the Pacific coast of S.
America, that have exhibited recent cooling (Falvey and Garreaud, 2009). Of specific importance for the
early global records, large (> 1°C) summer time warm bias adjustments for many European 19th century and
early 20th century records were revisited and broadly confirmed by a range of approaches (Bohm et al.,
2010; Brunet et al., 2011).
Since AR4 efforts have also been made to interpolate Antarctic records from the sparse, predominantly
coastal ground-based network (Chapman and Walsh, 2007; Monaghan et al., 2008; Steig et al., 2009;
O'Donnell et al., 2011). Although these agree that Antarctica as a whole has warmed since the late 1950s,
substantial multi-annual to multi-decadal variability and uncertainties in reconstructed magnitude and spatial
trend structure yield only low confidence in the details of pan-Antarctic regional LSAT changes.
In summary, it is certain that globally averaged LSAT has risen since the late 19th century and that this
warming has been particularly marked since the 1970s. Several independently analyzed global and regional
LSAT data products support this conclusion. There is low confidence in changes prior to 1880 owing to the
reduced number of estimates, non-standardized measurement techniques, the greater spread among the
estimates, and particularly the greatly reduced observational sampling. Confidence is also low in the spatial
detail and magnitude of LSAT trends in sparsely sampled regions such as Antarctica. Since AR4 significant
efforts have been undertaken to identify and adjust for data issues and new estimates have been produced.
These innovations have further strengthened overall understanding of the global LSAT records.
2.4.1.2
Diurnal Temperature Range
In AR4 Diurnal Temperature Range (DTR) was found, globally, to have narrowed since 1950 with minimum
daily temperatures increasing faster than maximum daily temperatures. However, significant multi-decadal
variability was highlighted including a recent period from 1997 to 2004 of no change, as both maximum and
minimum temperatures rose at similar rates. The Technical Summary of AR4 highlighted changes in DTR
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and their causes as a key uncertainty. Since AR4 uncertainties in DTR and its physical interpretation have
become even more apparent.
No dedicated global analysis of DTR has been undertaken subsequent to Vose et al. (2005a) although global
behaviour has been discussed in two broader ranging analyses. Rohde et al. (2012) note an apparent reversal
since the mid-1980s; with DTR subsequently increasing. This decline and subsequent increase in DTR over
global land surfaces is qualitatively consistent with the dimming and subsequent brightening noted in Section
2.3.3.1. Donat et al. (2013c) using HadEX2 (Section 2.6) find significant decreasing DTR trends in over half
of the land areas assessed but less than 10% of land with significant increases since 1951. Available trend
estimates (–0.04 ± 0.01°C per decade over 1950–2011 (Rohde et al., 2012), –0.066°C per decade over 1950–
2004 (Vose et al., 2005a), are much smaller than global mean LSAT average temperature trends over 1950–
2012 (Table 2.4). It therefore logically follows that globally averaged maximum and minimum temperatures
over land have both increased by in excess of 0.1°C per decade since 1950.
Regionally, Makowski et al. (2008) found that DTR behaviour in Europe over 1950 to 2005 changed from a
decrease to an increase in the 1970s in Western Europe and in the 1980s in Eastern Europe. Sen Roy and
Balling (2005) found significant increases in both maximum and minimum temperatures for India, but little
change in DTR over 1931–2002. Christy et al. (2009) reported that for East Africa there has been no pause in
the narrowing of DTR in recent decades. Zhou and Ren (2011) reported a significant decrease in DTR over
mainland China of –0.15°C per decade during 1961-2008.
Various investigators (e.g., Christy et al. (2009), Pielke and Matsui (2005), Zhou and Ren (2011)) have
raised doubts about the physical interpretation of minimum temperature trends, hypothesizing that
microclimate and local atmospheric composition impacts are more apparent because the dynamical mixing at
night is much reduced. Parker (2006) investigated this issue arguing that if data were affected in this way,
then a trend difference would be expected between calm and windy nights. However, he found no such
minimum temperature differences on a global average basis. Using more complex boundary layer modelling
techniques Steeneveld et al. (2011) and McNider et al. (2012) showed much lower sensitivity to windspeed
variations than posited by Pielke and Matsui but both concluded that boundary layer understanding was key
to understanding the minimum temperature changes. Data analysis and long-term side-by-side
instrumentation field studies show that real non-climatic data artefacts certainly affect maximum and
minimum differently in the raw records for both recent (Fall et al., 2011; Williams et al., 2012) and older
(Bohm et al., 2010; Brunet et al., 2011) records. Hence there could be issues over interpretation of apparent
DTR trends and variability in many regions (Christy et al., 2006; Christy et al., 2009; Fall et al., 2011; Zhou
and Ren, 2011; Williams et al., 2012), particularly when accompanied by regional-scale Land Use / Land
Cover (LULC) changes (Christy et al., 2006).
In summary, confidence is medium in reported decreases in observed global Diurnal Temperature Range
(DTR), noted as a key uncertainty in AR4. Several recent analyses of the raw data on which many previous
analyses were based point to the potential for biases that differently affect maximum and minimum average
temperatures. However, apparent changes in DTR are much smaller than reported changes in average
temperatures and therefore it is virtually certain that maximum and minimum temperatures have increased
since 1950.
2.4.1.3
Land-Use Change and Urban Heat Island Effects
In AR4 Urban Heat Island (UHI) effects were concluded to be real local phenomena with negligible impact
on large-scale trends. UHI and land-use land-cover change (LULC) effects arise mainly because the
modified surface affects the storage and transfer of heat, water and airflow. For single discrete locations
these impacts may dominate all other factors.
Regionally, most attention has focused upon China. A variety of investigations have used methods as diverse
as sea surface temperature comparisons (e.g., Jones et al., 2008), urban minus rural (e.g., Ren et al., 2008;
Yang et al., 2011), satellite observations (Ren and Ren, 2011) and observations minus reanalysis (e.g., Hu et
al., 2010; Yang et al., 2011). Interpretation is complicated because often studies have used distinct versions
of station series. For example the effect in Beijing is estimated at 80% (Ren et al., 2007) or 40% (Yan et al.,
2010) of the observed trend depending upon data corrections applied. A representative sample of these
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studies suggest the effect of UHI and LULC is approximately 20% of the trend in Eastern China as a whole
and of the order 0.1°C per decade nationally (Table 1 in Yang et al., 2011) over the last 30 years, but with
very substantial uncertainties. These effects have likely been partially or completely accounted for in many
homogenized series (e.g., Li et al., 2010b; Yan et al., 2010). Fujibe (2009) ascribes about 25% of Japanese
warming trends in 1979–2006 to UHI effects. Das et al. (2011) confirmed that many Japanese sites have
experienced UHI warming but that rural stations show unaffected behaviour when compared to nearby SSTs.
There is an important distinction to be made between UHI trend effects in regions underseeing rapid
development and those which have been developed for a long time. Jones and Lister (2009) and Wilby et al.
(2011) using data from London (UK) concluded that some sites which have always been urban and where
the UHI has not grown in magnitude will exhibit regionally indicative trends that agree with nearby rural
locations and that in such cases the time series may exhibit multi-decadal trends driven primarily by synoptic
variations. A lack of obvious time-varying UHI influences was also noted for Sydney, Melbourne and
Hobart in Australia by Trewin (2012). The impacts of urbanization also will be dependent upon the natural
LULC characteristics that they replace. Zhang et al. (2010) found no evidence for urban influences in the
desert North West region of China despite rapid urbanization.
Global adjusted datasets likely account for much of the UHI effect present in the raw data. For the US
network, Hausfather et al. (2013) showed that the adjustments method used in GHCNv3 removed much of an
apparent systematic difference between urban and rural locations, concluding that this arose from adjustment
of biased urban location data. Globally, Hansen et al. (2010) used satellite-based nightlight radiances to
estimate the worldwide influence on LSAT of local urban development. Adjustments only reduced the global
1900–2009 temperature change (averaged over land and ocean) from 0.71°C to 0.70°C. Wickham et al.
(2013) also used satellite data and found that urban locations in the Berkeley dataset exhibited even less
warming than rural stations, although not statistically significantly so, over 1950 to 2010.
Studies of the broader effects of LULC since AR4 have tended to focus upon the effects of irrigation on
temperatures, with a large number of studies in the Californian central belt (Christy et al., 2006; Kueppers et
al., 2007; Bonfils et al., 2008; Lo and Famiglietti, 2013). They find cooler average temperatures and a
marked reduction in DTR in areas of active irrigation and ascribe this to increased humidity; effectively a
repartitioning of moist and dry energy terms. Reanalyses have also been used to estimate the LULC
signature in LSAT trends. Fall et al. (2010) found that the North American Regional Reanalysis generated
overall surface air temperature trends for 1979–2003 similar to observed records. Observations-minusreanalysis trends were most positive for barren and urban areas, in accord with Lim et al. (2008)’s results
using the NCEP/NCAR and ERA-40 reanalyses, and negative in agricultural areas.
McKitrick and Michaels (2004) and de Laat and Maurellis (2006) assessed regression of trends with national
socioeconomic and geographical indicators, concluding that UHI and related LULC have caused much of the
observed LSAT warming. AR4 concluded that this correlation ceases to be statistically significant if one
takes into account the fact that the locations of greatest socioeconomic development are also those that have
been most warmed by atmospheric circulation changes but provided no explicit evidence for this overall
assessment result. Subsequently McKitrick and Michaels (2007) concluded that about half the reported
warming trend in global-average land surface air temperature in 1980–2002 resulted from local land-surface
changes and faults in the observations. Schmidt (2009) undertook a quantitative analysis that supported AR4
conclusions that much of the reported correlation largely arose due to naturally occurring climate variability
and model over-fitting and was not robust. Taking these factors into account, modified analyses by
McKitrick (2010) and McKitrick and Nierenberg (2010) still yielded significant evidence for such
contamination of the record.
In marked contrast to regression based studies, several studies have shown the methodologically diverse set
of modern reanalysis products and the various LSAT records at global and regional levels to be similar since
at least the mid-20th century (Simmons et al., 2010; Parker, 2011; Ferguson and Villarini, 2012; Jones et al.,
2012; Vose et al., 2012a). These reanalyses do not directly assimilate the LSAT measurements but rather
infer LSAT estimates from an observational constraint provided by much of the rest of the global observing
system, thus representing an independent estimate. These reanalysis products on average imply slightly
more, rather than significantly less, warming than the observational datasets. A hypothesized residual
significant warming artefact argued for by regression-based analyses is therefore physically inconsistent with
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many other components of the global observing system according to a broad range of state-of-the-art data
assimilation models (Box 2.3). Further, Efthymiadis and Jones (2010) estimated an absolute upper limit on
urban influence globally of 0.02°C per decade, or ~15% of the total LSAT trends, in 1951–2009 from trends
of coastal land and sea surface temperature.
In summary, it is indisputable that UHI and LULC are real influences on raw temperature measurements. At
question is the extent to which they remain in the global products (as residual biases in broader regionally
representative change estimates). Based primarily upon the range of urban minus rural adjusted dataset
comparisons and the degree of agreement of these products with a broad range of reanalysis products, it is
unlikely that any uncorrected urban heat-island effects and LULC change effects have raised the estimated
centennial globally averaged LSAT trends by more than 10% of the reported trend (high confidence, based
on robust evidence and high agreement). This is an average value; in some regions with rapid development,
UHI and LULC change impacts on regional trends may be substantially larger.
2.4.2
Sea Surface Temperature and Marine Air Temperature
AR4 concluded that ‘recent’ warming (since the 1950s) is strongly evident at all latitudes in SST over each
ocean. Prominent spatio-temporal structures including the ENSO and Pacific Decadal variability patterns in
the Pacific Ocean (Box 2.5) and a hemispheric asymmetry in the Atlantic Ocean were highlighted as
contributors to the regional differences in surface warming rates, which in turn affect atmospheric
circulation. Since AR4 the availability of metadata has increased, data completeness has improved and a
number of new SST products have been produced. Inter-comparisons of data obtained by different
measurement methods, including satellite data, have resulted in better understanding of errors and biases in
the record.
2.4.2.1
Advances in Assembling Datasets and in Understanding Data Errors
2.4.2.1.1 In situ data records
Historically, most SST observations were obtained from moving ships. Buoy measurements comprise a
significant and increasing fraction of in situ SST measurements from the 1980s onward (Figure 2.15).
Improvements in the understanding of uncertainty have been expedited by the use of metadata (Kent et al.,
2007) and the recovery of observer instructions and other related documents. Early data were systematically
cold biased because they were made using canvas or wooden buckets that, on average, lost heat to the air
before the measurements were taken. This effect has long been recognized (Brooks, 1926), and prior to AR4
represented the only artefact adjusted in gridded SST products, like HadSST2 (Rayner et al., 2006) and
ERSST (Smith et al., 2005; 2008), which were based on “bucket correction” methods by Folland and Parker
(1995) and Smith and Reynolds (2002), respectively. The adjustments, made using ship observations of
Night Marine Air Temperature data (NMAT) and other sources, had a striking effect on the SST global mean
estimates: note the difference in 1850–1941 between HadSST2 and International Comprehensive OceanAtmosphere Dataset (ICOADS) curves in Figure 2.16 (a brief description of SST and NMAT datasets and
their methods is given in Supplementary Material 2.SM.4.3).
[INSERT FIGURE 2.15 HERE]
Figure 2.15: Temporal changes in the prevalence of different measurement methods in the International
Comprehensive Ocean-Atmosphere Dataset (ICOADS). a) fractional contributions of observations made by different
measurement methods: bucket observations (blue), engine room intake (ERI) and hull contact sensor observations
(green), moored and drifting buoys (red), and unknown (yellow). b) Global annual average Sea Surface Temperature
(SST) anomalies based on different kinds of data: ERI and hull contact sensor (green), bucket (blue), buoy (red), and all
(black). Averages are computed over all 5°  5° where both ERI/hull and bucket measurements, but not necessarily
buoy data, were available. Adapted from Kennedy et al. (2011a).
Buckets of improved design and measurement methods with smaller, on average, biases came into use after
1941 (Figure 2.15, top); average biases were reduced further in recent decades, but not eliminated (Figure
2.15, bottom). Increasing density of SST observations made possible the identification (Reynolds et al.,
2002; Reynolds et al., 2010; Kennedy et al., 2012) and partial correction of more recent period biases
(Kennedy et al., 2011a). In particular, it is hypothesized that the proximity of the hot engine often biases
engine room intake (ERI) measurements warm (Kent et al., 2010). Because of the prevalence of the ERI
measurements among SST data from ships, the ship SSTs are biased warm by 0.12°C–0.18°C on average
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compared to the buoy data (Reynolds et al., 2010; Kennedy et al., 2011a; Kennedy et al., 2012). An
assessment of the potential impact of modern biases can be ascertained by considering the difference
between HadSST3 (bias corrections applied throughout) and HadSST2 (bucket corrections only) global
means (Figure 2.16): it is particularly prominent in 1945–1970 period, when rapid changes in prevalence of
ERI and bucket measurements during and after the World War II affect HadSST2 due to the uncorrected
measurement biases (Thompson et al., 2008), while these are corrected in HadSST3. Nevertheless, for
periods longer than a century the effect of HadSST3-HadSST2 differences on linear trend slopes is small
relative to the trend uncertainty (Table 2.5). Some degree of independent check on the validity of HadSST3
adjustments comes from a comparison to sub-surface temperature data (Gouretski et al., 2012; Section 3.2).
Table 2.5: Trend estimates and 90% confidence intervals (Box 2.2) for two subsequent versions of the HadSST dataset
over five common periods. HadSST2 has been used in AR4; HadSST3 is used in this chapter.
Trends in °C per decade
Dataset
HadSST3 (Kennedy et al., 2011a)
HadSST2 (Rayner et al., 2006)
1880–2012
0.054 ± 0.012
0.051 ± 0.015
1901–2012
0.067 ± 0.013
0.069 ± 0.012
1901–1950
0.117 ± 0.028
0.084 ± 0.055
1951–2012
0.074 ± 0.027
0.098 ± 0.017
1979–2012
0.124 ± 0.030
0.121 ± 0.033
[INSERT FIGURE 2.16 HERE]
Figure 2.16: Global annual average Sea Surface Temperature (SST) and Night Marine Air Temperature (NMAT)
relative to a 1961–1990 climatology from gridded datasets of SST observations (HadSST2 and its successor HadSST3),
the raw SST measurement archive (ICOADS, v2.5) and night marine air temperatures dataset HadNMAT2 (Kent et al.,
2013). HadSST2 and HadSST3 both are based on SST observations from versions of the ICOADS dataset, but differ in
degree of measurement bias correction.
The traditional approach to modeling random error of in situ SST data assumed the independence of
individual measurements. Kent and Berry (2008) identified the need to account for error correlation for
measurements from the same “platform” (i.e., an individual ship or buoy), but change from platform to
platform in a random fashion. Kennedy et al. (2011b) achieved that by introducing platform-dependent
biases, which are constant within the same platform, but change randomly from one platform to another.
Accounting for such correlated errors in HadSST3 resulted in estimated error for global and hemispheric
monthly means that are more than twice the estimates given by HadSST2. The uncertainty in many, but not
all, components of the HadSST3 product is represented by the ensemble of its realizations (Figure 2.17).
Datasets of Marine Air Temperatures (MAT) have traditionally been restricted to night-time series only
(NMAT datasets) due to the direct solar heating effect on the daytime measurements, although corrected
daytime MAT records for 1973-present are already available (Berry and Kent, 2009). Other major biases,
affecting both night-time and daytime MAT are due to increasing deck height with the general increase in
the size of ships over time and non-standard measurement practices. Recently these biases were re-examined
and explicit uncertainty calculation undertaken for NMAT by Kent et al. (2013), resulting in the
HadNMAT2 dataset.
2.4.2.1.2 Satellite SST data records
Satellite SST datasets are based on measuring electromagnetic radiation that left ocean surface and got
transmitted through the atmosphere. Because of complexity of processes involved, the majority of such data
has to be calibrated on the basis of in situ observations. The resulting datasets, however, provide a
description of global SST fields with a level of spatial detail unachievable by in situ data only. The principal
Infra-Red (IR) sensor is the Advanced Very High Resolution Radiometer (AVHRR) series. Since AR4, the
AVHRR time series has been reprocessed consistently back to March 1981 (Casey et al., 2010) to create the
AVHRR Pathfinder v5.2 dataset. Passive microwave datasets of SST are available since 1997 equatorward
of 40° and near-globally since 2002 (Wentz et al., 2000; Gentemann et al., 2004). They are generally less
accurate than IR-based SST datasets, but their superior coverage in areas of persistent cloudiness provides
SST estimates where the IR record has none (Reynolds et al., 2010).
The (Advanced) Along Track Scanning Radiometer ((A)ATSR) series of three sensors was designed for
climate monitoring of SST; their combined record starts in August 1991 and exceeds two decades (it stopped
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with the demise of the ENVISAT platform in 2012). The (A)ATSRs are ‘dual-view’ IR radiometers intended
to allow atmospheric effects removal without the use of in situ observations. Since AR4, ATSR observations
have been reprocessed with new estimation techniques (Embury and Merchant, 2011). The resulting SST
products seem to be more accurate than many in situ observations (Embury et al., 2011). In terms of monthly
global means, the agreement is illustrated in Figure 2.17. By analyzing AATSR and in situ data together,
Kennedy at al. (2012) verified and extended existing models for biases and random errors of in situ data.
[INSERT FIGURE 2.17 HERE]
Figure 2.17: Global monthly mean Sea Surface Temperature (SST) anomalies relative to a 1961–1990 climatology
from satellites (ATSRs) and in situ records (HadSST3). Black lines: the 100-member HadSST3 ensemble. Red lines:
ATSR-based night-time subsurface temperature at 0.2m depth (SST0.2m) estimates from the ATSR Reprocessing for
Climate (ARC) project. Retrievals based on three spectral channels (D3, solid line) are more accurate than retrievals
based on only two (D2, dotted line). Contributions of the three different ATSR missions to the curve shown are
indicated at the bottom. The in situ and satellite records were co-located within 5° × 5° monthly grid boxes: only those
where both datasets had data for the same month were used in the comparison. Adapted from Merchant et al. (2012).
2.4.2.2
Interpolated SST Products and Trends
SST datasets form a major part of global surface temperature analyses considered in this assessment report.
To use an SST dataset as a boundary condition for atmospheric reanalyses products (Box 2.3) or in
atmosphere-only climate simulations (considered in Chapter 9 onwards), gridded datasets with complete
coverage over the global ocean are typically needed. These are usually based on a special form of kriging
(optimal interpolation) procedure that retains large-scale correlation structures and can accommodate very
sparse data coverage. For the pre-satellite era (generally, before October 1981) only in situ data are used; for
the latter period some products also use AVHRR data. Figure 2.18 compares interpolated SST datasets that
extend back to the 19th century with the uninterpolated HadSST3 and HadNMAT2 products. Linear trend
estimates for global mean SSTs from those products updated through 2012 are presented in Table 2.6.
Differences between the trends from different datasets are larger when the calculation period is shorter
(1979-2012) or has lower quality data (1901-1950); these are mainly due to different data coverage of
underlying observational datasets and bias correction methods used in these products.
[INSERT FIGURE 2.18 HERE]
Figure 2.18: Global annual average Sea Surface Temperature (SST) and Night Marine Air Temperature (NMAT)
relative to a 1961–1990 climatology from state of the art datasets. Spatially interpolated products are shown by solid
lines; non-interpolated products by dashed lines.
Table 2.6: Trend estimates and 90% confidence intervals (Box 2.2) for interpolated SST datasets (uninterpolated stateof-the-art HadSST3 dataset is included for comparison). Dash indicates not enough data available for trend calculation.
Trends in °C per decade
Dataset
1880–2012
1901–2012
1901–1950
1951–2012
1979–2012
HadISST (Rayner et al., 2003)
0.042 ± 0.007
0.052 ± 0.007
0.067 ± 0.024
0.064 ± 0.015
0.072 ± 0.024
0.058 ± 0.007
0.066 ± 0.032
0.071 ± 0.014
0.073 ± 0.020
COBE-SST (Ishii et al., 2005)
ERSSTv3b (Smith et al., 2008)
0.054 ± 0.015
0.071 ± 0.011
0.097 ± 0.050
0.088 ± 0.017
0.105 ± 0.031
HadSST3 (Kennedy et al., 2011a)
0.054 ± 0.012
0.067 ± 0.013
0.117 ± 0.028
0.074 ± 0.027
0.124 ± 0.030
In summary, it is certain that global average Sea Surface Temperatures (SSTs) have increased since the
beginning of the 20th century. Since AR4, major improvements in availability of metadata and data
completeness have been made, and a number of new global SST records have been produced.
Intercomparisons of new SST data records obtained by different measurement methods, including satellite
data, have resulted in better understanding of uncertainties and biases in the records. While these innovations
have helped highlight and quantify uncertainties and affect our understanding of the character of changes
since the mid-20th century, they do not alter the conclusion that global SSTs have increased both since the
1950s and since the late 19th century.
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2.4.3
Chapter 2
IPCC WGI Fifth Assessment Report
Global Combined Land and Sea Surface Temperature
AR4 concluded that the GMST had increased, with the last 50 years increasing at almost double the rate of
the last 100 years. Subsequent developments in LSAT and SST have led to better understanding of the data
and their uncertainties as discussed in preceding sections. This improved understanding has led to revised
global products.
Changes have been made to all three GMST datasets that were used in AR4 (Hansen et al., 2010; Morice et
al., 2012; Vose et al., 2012b). These are now in somewhat better agreement with each other over recent
years, in large part because HadCRUT4 now better samples the NH high latitude land regions (Jones et al.,
2012; Morice et al., 2012) which comparisons to reanalyses had shown led to a propensity for HadCRUT3 to
under-estimate recent warming (Simmons et al., 2010). Starting in the 1980s each decade has been
significantly warmer than all preceding decades since the 1850s in HadCRUT4, a dataset that explicitly
quantifies a large number of sources of uncertainty (Figure 2.19). Each of the last three decades is also the
warmest in the other two GMST datasets, but these have substantially less mature and complete uncertainty
estimates, precluding such an assessment of significance of their decadal differences. The GISS and MLOST
datasets fall outside the 90% CI of HadCRUT4 for several decades in the twentieth century (Figure 2.19).
These decadal differences could reflect residual biases in one or more dataset, an incomplete treatment of
uncertainties in HadCRUT4.1, or a combination of these effects (Box 2.1). The datasets utilize different
LSAT (Section 2.4.1) and SST (Section 2.4.2) component records (Supplementary Material 2.SM.4.3.4) that
in the case of SST differ somewhat in their multi-decadal trend behaviour (Table 2.6 compare HadSST3 and
ERSSTv3b).
All ten of the warmest years have occurred since 1997, with 2010 and 2005 effectively tied for the warmest
year on record in all three products. However, uncertainties on individual annual values are sufficiently large
that the ten warmest years are statistically indistinguishable from one another. The global-mean trends are
significant for all datasets and multi-decadal periods considered in Table 2.7. Using HadCRUT4 and its
uncertainty estimates, the warming from 1850–1900 (early-industrial) to 1986–2005 (reference period for the
modelling chapters and Annex I) is 0.61°C ± 0.06°C (90% confidence interval) (Supplementary Material
2.SM.4.3.3).
Table 2.7: Same as Table 2.4, but for Global Mean Surface Temperature (GMST) over five common periods.
Trends in °C per decade
Dataset
1880–2012
1901–2012
1901–1950
1951–2012
1979–2012
HadCRUT4 (Morice et al., 2012)
0.062 ± 0.012 0.075 ± 0.013 0.107 ± 0.026 0.106 ± 0.027 0.155 ± 0.033
NCDC MLOST (Vose et al., 2012b) 0.064 ± 0.015 0.081 ± 0.013 0.097 ± 0.040 0.118 ± 0.021 0.151 ± 0.037
GISS (Hansen et al., 2010)
0.065 ± 0.015 0.083 ± 0.013 0.090 ± 0.034 0.124 ± 0.020 0.161 ± 0.033
[INSERT FIGURE 2.19 HERE]
Figure 2.19: Decadal Global Mean Surface Temperature (GMST) anomalies (white vertical lines in grey blocks) and
their uncertainties (90% confidence intervals as grey blocks) based upon the Land-Surface Air Temperature (LSAT)
and Sea Surface Temperature (SST) combined HadCRUT4 (v4.1.1.0) ensemble (Morice et al., 2012). Anomalies are
relative to a 1961–1990 climatology.1850s indicates the period 1850-1859, and so on. NCDC MLOST and GISS
dataset best-estimates are also shown.
Differences between datasets are much smaller than both interannual variability and the long-term trend
(Figure 2.20). Since 1901 almost the whole globe has experienced surface warming (Figure 2.21). Warming
has not been linear; most warming occurred in two periods: around 1900 to around 1940 and around 1970
onwards (Figure 2.22. Shorter periods are noisier and so proportionately less of the sampled globe exhibits
statistically significant trends at the grid box level (Figure 2.22). The two periods of global mean warming
exhibit very distinct spatial signatures. The early 20th century warming was largely a NH mid- to highlatitude phenomenon, whereas the more recent warming is more global in nature. These distinctions may
yield important information as to causes (Chapter 10). Differences between datasets are larger in earlier
periods (Figures 2.19, 2.20), particularly prior to the 1950s when observational sampling is much more
geographically incomplete (and many of the well sampled areas may have been globally unrepresentative
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(Bronnimann, 2009)), data errors and subsequent methodological impacts are larger (Thompson et al., 2008),
and different ways of accounting for data void regions are more important (Vose et al., 2005b).
[INSERT FIGURE 2.20 HERE]
Figure 2.20: Annual Global Mean Surface Temperature (GMST) anomalies relative to a 1961–1990 climatology from
the latest version of the three combined Land-Surface Air Temperature (LSAT) and Sea Surface Temperature (SST)
datasets (HadCRUT4, GISS and NCDC MLOST). Published dataset uncertainties are not included for reasons
discussed in Box 2.1.
[INSERT FIGURE 2.21 HERE]
Figure 2.21: Trends in Global Mean Surface Temperature (GMST) from the three datasets of Figure 2.20 for 1901–
2012. White areas indicate incomplete or missing data. Trends have been calculated only for those grid boxes with
greater than 70% complete records and more than 20% data availability in first and last decile of the period. Black plus
signs (+) indicate grid boxes where trends are significant (i.e., a trend of zero lies outside the 90% confidence interval).
Differences in coverage primarily reflect the degree of interpolation to account for data void regions undertaken by the
dataset providers ranging from none beyond grid box averaging (HadCRUT4) to substantial (GISS).
[INSERT FIGURE 2.22 HERE]
Figure 2.22: Trends in Global Mean Surface Temperature (GMST) from NCDC MLOST for three non-consectutive
shorter periods (1911–1940; 1951–1980; 1981–2012). White areas indicate incomplete or missing data. Trends and
significance have been calculated as in Figure 2.22.
Much interest has focussed on the period since 1998 and an apparent flattening (‘hiatus’) in trends, most
marked in NH winter (Cohen et al., 2012). Various investigators have pointed out the limitations of such
short-term trend analysis in the presence of auto-correlated series variability and that several other similar
length phases of no warming exist in all the observational records and in climate model simulations
(Easterling and Wehner, 2009; Peterson et al., 2009; Liebmann et al., 2010; Foster and Rahmstorf, 2011;
Santer et al., 2011). This issue is discussed in the context of model behaviour, forcings and natural variability
in Box 9.2 and Section 10.3.1. Regardless, all global combined land and sea surface temperature datasets
exhibit a statistically nonsignificant warming trend over 1998–2012 (0.042°C ± 0.093°C per decade
(HadCRUT4); 0.037°C ± 0.085°C per decade (NCDC MLOST); 0.069°C ± 0.082°C per decade (GISS)). An
average of the trends from these three data sets yields an estimated change for the 1998–2012 period of
0.05°C [–0.05 to +0.15] per decade.
In summary, it is certain that globally averaged near surface temperatures have increased since the late 19th
century. Each of the past three decades has been warmer than all the previous decades in the instrumental
record, and the decade of the 2000s has been the warmest. The global combined land and ocean temperature
data show an increase of about 0.89°C (0.69°C–1.08°C) over the period 1901–2012 and about 0.72°C
(0.49°C–0.89°C) over the period 1951–2012 when described by a linear trend. Despite the robust multidecadal timescale warming, there exists substantial multi-annual variability in the rate of warming with
several periods exhibiting almost no linear trend (including the warming hiatus since 1998).
2.4.4
Upper Air Temperature
AR4 summarized that globally the troposphere had warmed at a rate greater than the GMST over the
radiosonde record, while over the shorter satellite era the GMST and tropospheric warming rates were
indistinguishable. Trends in the tropics were more uncertain than global trends although even this region was
concluded to be warming. Globally, the stratosphere was reported to be cooling over the satellite era starting
in 1979. New advances since AR4 have highlighted the substantial degree of uncertainty in both satellite and
balloon-borne radiosonde records and lead to some revisions and improvements in existing products and the
creation of a number of new data products.
2.4.4.1
Advances in Multi-Decadal Observational Records
The major global radiosonde records extend back to 1958 with temperatures, measured as the balloon
ascends, reported at mandatory pressure levels. Satellites have monitored tropospheric and lower
stratospheric temperature trends since late 1978 through the Microwave Sounding Unit (MSU) and its
follow-on Advanced Microwave Sounding Unit (AMSU) since 1998. These measures of upwelling radiation
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represent bulk (volume averaged) atmospheric temperature (Figure 2.23). The ‘Mid-Tropospheric’ (MT)
MSU channel that most directly corresponds to the troposphere has 10–15% of its signal from both the skin
temperature of the Earth’s surface and the stratosphere. Two alternative approaches have been suggested for
removing the stratospheric component based upon differencing of view angles (LT) and statistical
recombination (*G) with the ‘Lower Stratosphere’ (LS) channel (Spencer and Christy, 1992; Fu et al., 2004).
The MSU satellite series also included a Stratospheric Sounding Unit (SSU) that measured at higher altitudes
(Seidel et al., 2011).
[INSERT FIGURE 2.23 HERE]
Figure 2.23: Vertical weighting functions for those satellite temperature retrievals discussed in this chapter (modified
from Seidel et al. (2011)). The dashed line indicates the typical maximum altitude achieved in the historical radiosonde
record. The three SSU channels are denoted by the designated names 25, 26 and 27. LS (Lower Stratosphere) and MT
(Mid Troposphere) are two direct MSU measures and LT (Lower Troposphere) and *G (Global Troposphere) are
derived quantities from one or more of these that attempt to remove the stratospheric component from MT.
At the time of AR4 there were only two ‘global’ radiosonde datasets that included treatment of homogeneity
issues: RATPAC (Free et al., 2005) and HadAT (Thorne et al., 2005). Three additional estimates have
appeared since AR4 based on novel and distinct approaches. A group at the University of Vienna have
produced RAOBCORE and RICH (Haimberger, 2007; Haimberger et al., 2008, 2012) using ERA reanalysis
products (Box 2.3). Sherwood and colleagues developed an iterative universal kriging approach for
radiosonde data to create IUK (Sherwood et al., 2008) and concluded that non-climatic data issues leading to
spurious cooling remained in the deep tropics even after homogenisation. The HadAT group created an
automated version, undertook systematic experimentation and concluded that the parametric uncertainty
(Box 2.1) was of the same order of magnitude as the apparent climate signal (McCarthy et al., 2008; Titchner
et al., 2009; Thorne et al., 2011). A similar ensemble approach has also been applied to the RICH product
(Haimberger et al., 2012). These various ensembles and new products exhibit more tropospheric warming /
less stratospheric cooling than pre-existing products at all levels. Globally the radiosonde records all imply
the troposphere has warmed and the stratosphere cooled since 1958 but with uncertainty that grows with
height and is much greater outside the better-sampled NH extra-tropics (Thorne et al., 2011; Haimberger et
al., 2012), where it is of the order 0.1°C per decade.
For MSU, AR4 considered estimates produced from three groups: UAH (University of Alabama in
Huntsville); RSS (Remote Sensing Systems) and VG2 (now no longer updated). A new product has been
created by NOAA labelled STAR, using a fundamentally distinct approach for the critical inter-satellite
warm target calibration step (Zou et al., 2006a). STAR exhibits more warming / less cooling at all levels than
UAH and RSS. For MT and LS, Zou and Wang (2010) concluded that this does not primarily relate to use of
their inter-satellite calibration technique but rather differences in other processing steps. RSS also produced a
parametric uncertainty ensemble (Box 2.1) employing a Monte-Carlo approach allowing methodological
inter-dependencies to be fully expressed (Mears et al., 2011). For large-scale trends dominant effects were
inter-satellite offset determinations and, for tropospheric channels, diurnal drift. Uncertainties were
concluded to be of the order 0.1°C per decade at the global mean for both tropospheric channels (where it is
of comparable magnitude to the long-term trends) and the stratospheric channel.
SSU provides the only long-term near-global temperature data above the lower stratosphere, with the series
terminating in 2006. Some AMSU-A channels have replaced this capability and efforts to understand the
effect of changed measurement properties have been undertaken (Kobayashi et al., 2009). Until recently only
one SSU dataset existed (Nash and Edge, 1989), updated by Randel et al. (2009). Liu and Weng (2009) have
produced an intermediate analysis for Channels 25 and 26 (but not channel 27). Wang et al. (2012g) building
upon insights from several of these recent studies have produced a more complete analysis. Differences
between the independent estimates are much larger than differences between MSU records or radiosonde
records at lower levels, with substantial inter-decadal time series behaviour departures, zonal trend structure,
and global trend differences of the order 0.5°C per decade (Seidel et al., 2011; Thompson et al., 2012; Wang
et al., 2012g). Although all SSU datasets agree that the stratosphere is cooling, there is therefore low
confidence in the details above the lower stratosphere.
In summary, many new datasets have been produced since AR4 from radiosondes and satellites with
renewed interest in satellite measurements above the lower stratosphere. Several studies have attempted to
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quantify the parametric uncertainty (Box 2.1) more rigorously. These various datasets and analyses have
served to highlight the degree of uncertainty in the data and derived products.
2.4.4.2
Intercomparisons of Various Long-Term Radiosonde and MSU Products
Since AR4 there have been a large number of intercomparisons between radiosonde and MSU datasets.
Interpretation is complicated, as most studies considered dataset versions that have since been superseded.
Several studies compared UAH and RSS products to local, regional or global raw / homogenized radiosonde
data (Christy and Norris, 2006; Christy et al., 2007; Randall and Herman, 2008; Christy and Norris, 2009;
Christy et al., 2010; Christy et al., 2011; Mears et al., 2012; Po-Chedley and Fu, 2012). Early studies
focussed upon the time of transition from NOAA-11 to NOAA-12 (early 1990s), which indicated an
apparent issue in RSS. Christy et al. (2007) noted that this coincided with the Mount Pinatubo eruption and
that RSS was the only product, either surface or tropospheric, that exhibited tropical warming immediately
after the eruption when cooling would be expected. Using reanalysis data Bengtsson and Hodges (2011) also
found evidence of a potential jump in RSS in 1993 over the tropical oceans. Mears et al. (2012) cautioned
that an El Niño event quasi-simultaneous with Pinatubo complicates interpretation. They also highlighted
several other periods of disagreement between radiosonde records and MSU records. All MSU records were
most uncertain when satellite orbits are drifting rapidly (Christy and Norris, 2006, 2009). Mears et al. (2011)
found that trend differences between RSS and other datasets could not be explained in many cases by
parametric uncertainties in RSS alone. It was repeatedly cautioned that there were potential common biases
(of varying magnitude) between the different MSU records or between the different radiosonde records
which complicate intercomparisons (Christy and Norris, 2006, 2009; Mears et al., 2012).
In summary, assessment of the large body of studies comparing various long-term radiosonde and MSU
products since AR4 is hampered by dataset version changes, and inherent data uncertainties. These factors
substantially limit the ability to draw robust and consistent inferences from such studies about the true longterm trends or the value of different data products.
2.4.4.3
Additional Evidence from Other Technologies and Approaches
Global Positioning System (GPS) radio occultation (RO) currently represents the only self-calibrated SI
traceable raw satellite measurements (Anthes et al., 2008; Anthes, 2011). The fundamental observation is
time delay of the occulted signal’s phase traversing the atmosphere. The time delay is a function of several
atmospheric physical state variables. Subsequent analysis converts the time delay to temperature and other
parameters, which inevitably adds some degree of uncertainty to the derived temperature data.
Intercomparisons of GPS-RO products show that differences are largest for derived geophysical parameters
(including temperature), but are still small relative to other observing technologies (Ho et al., 2012).
Comparisons to MSU and radiosondes (Kuo et al., 2005; Ho et al., 2007; He et al., 2009; Ho et al., 2009b;
Ho et al., 2009a; Baringer et al., 2010; Sun et al., 2010; Ladstadter et al., 2011) show substantive agreement
in interannual behaviour, but also some multi-year drifts that require further examination before this
additional data source can usefully arbitrate between different MSU and radiosonde trend estimates.
Atmospheric winds are driven by thermal gradients. Radiosonde winds are far less affected by time-varying
biases than their temperatures (Gruber and Haimberger, 2008; Sherwood et al., 2008; Section 2.7.3). Allen
and Sherwood (2007) initially used radiosonde wind to infer temperatures within the Tropical West Pacific
warm pool region, then extended this to a global analysis (Allen and Sherwood, 2008) yielding a distinct
tropical upper tropospheric warming trend maximum within the vertical profile, but with large uncertainty.
Winds can only quantify relative changes and require an initialization (location and trend at that location)
(Allen and Sherwood, 2008). The large uncertainty range was predominantly driven by this initialization
choice, a finding later confirmed by Christy et al. (2010), who in addition questioned the stability given the
sparse geographical sampling, particularly in the tropics, and possible systematic sampling effects amongst
other potential issues. Initializing closer to the tropics tended to reduce or remove the appearance of a
tropical upper tropospheric warming trend maximum (Allen and Sherwood, 2008; Christy et al., 2010).
There is only low confidence in trends inferred from ‘thermal winds’ given the relative immaturity of the
analyses and their large uncertainties.
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In summary, new technologies and approaches have emerged since AR4. However, these new technologies
and approaches either constitute too short a record or are too immature to inform assessments of long-term
trends at the present time.
2.4.4.4
Synthesis of Free Atmosphere Temperature Estimates
Global-mean lower tropospheric temperatures have increased since the mid-20th century (Figure 2.24,
bottom). Structural uncertainties (Box 2.1) are larger than at the surface but it can still be concluded that
globally the troposphere has warmed (Table 2.8). On top of this long-term trend are super-imposed shortterm variations that are highly correlated with those at the surface but of somewhat greater amplitude. Global
mean lower stratospheric temperatures have decreased since the mid-20th century punctuated by short-lived
warming events associated with explosive volcanic activity (Figure 2.24a). However, since the mid-1990s
little net change has occurred. Cooling rates are on average greater from radiosonde datasets than MSU
datasets. This very likely relates to widely recognized cooling biases in radiosondes (Mears et al., 2006)
which all dataset producers explicitly caution are likely to remain to some extent in their final products (Free
and Seidel, 2007; Haimberger et al., 2008; Sherwood et al., 2008; Thorne et al., 2011).
[INSERT FIGURE 2.24 HERE]
Figure 2.24: Global annual average lower stratospheric (top) and lower tropospheric (bottom) temperature anomalies
relative to a 1981–2010 climatology from different datasets. STAR does not produce a lower tropospheric temperature
product. Note that the y-axis resolution differs between the two panels.
Table 2.8: Trend estimates and 90% confidence intervals (Box 2.2) for radiosonde and MSU dataset global average
values over the radiosonde (1958–2012) and satellite periods (1979–2012). LT indicates Lower Troposphere, MT
indicates Mid Troposphere and LS indicates Lower Stratosphere (Figure 2.23. Satellite records only start in 1979 and
STAR do not produce an LT product.
Trends in °C per decade
1958–2012
1979–2012
Dataset
LT
MT
LS
LT
MT
LS
HadAT2 (Thorne 0.159 ± 0.038 0.095 ± 0.034 –0.339 ± 0.086 0.162 ± 0.047 0.079 ± 0.057 –0.436 ± 0.204
et al., 2005)
RAOBCORE 1.5 0.156 ± 0.031 0.109 ± 0.029 –0.186 ± 0.087 0.139 ± 0.049 0.079 ± 0.054 –0.266 ± 0.227
(Haimberger et
al., 2012)
RICH-obs
0.162 ± 0.031 0.102 ± 0.029 –0.285 ± 0.087 0.158 ± 0.046 0.081 ± 0.052 –0.331 ± 0.241
(Haimberger et
al., 2012)
RICH-tau
0.168 ± 0.032 0.111 ± 0.030 –0.280 ± 0.085 0.160 ± 0.046 0.083 ± 0.052 –0.345 ± 0.238
(Haimberger et
al., 2012)
RATPAC (Free et 0.136 ± 0.028 0.076 ± 0.028 –0.338 ± 0.092 0.128 ± 0.044 0.039 ± 0.051 –0.468 ± 0.225
al., 2005)
UAH (Christy et
0.138 ± 0.043 0.043 ± 0.042 –0.372 ± 0.201
al., 2003)
0.131 ± 0.045 0.079 ± 0.043 –0.268 ± 0.177
RSS (Mears and
Wentz, 2009a,
2009b)
STAR (Zou and
0.123 ± 0.047 –0.320 ± 0.175
Wang, 2011)
In comparison to the surface (Figure 2.22), tropospheric layers exhibit smoother geographic trends (Figure
2.25) with warming dominating cooling north of approximately 45°S and greatest warming in high northern
latitudes. The lower stratosphere cooled almost everywhere but this cooling exhibits substantial large-scale
structure. Cooling is greatest in the highest southern latitudes and smallest in high northern latitudes. There
are also secondary stratospheric cooling maxima in the mid-latitude regions of each hemisphere.
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[INSERT FIGURE 2.25 HERE]
Figure 2.25: Trends in MSU upper air temperature over 1979–2012 from UAH (left hand panels) and RSS (right hand
panels) and for LS (top row) and LT (bottom row). Data are temporally complete within the sampled domains for each
dataset. White areas indicate incomplete or missing data. Black plus signs (+) indicate grid boxes where trends are
significant (i.e., a trend of zero lies outside the 90% confidence interval).
Available global and regional trends from radiosondes since 1958 (Figure 2.26) show agreement that the
troposphere has warmed and the stratosphere cooled. While there is little ambiguity in the sign of the
changes, the rate and vertical structure of change are distinctly dataset-dependent, particularly in the
stratosphere. Differences are greatest in the tropics and SH extra-tropics where the historical radiosonde data
coverage is poorest. Not shown in the figure for clarity are estimates of parametric dataset uncertainties or
trend-fit uncertainties—both of which are of the order of at least 0.1°C per decade (Section 2.4.4.1).
[INSERT FIGURE 2.26 HERE]
Figure 2.26: Trends in upper air temperature for all available radiosonde data products that contain records for 1958–
2012 for the globe (top) and tropics (20°N–20°S) and extra-tropics (bottom). The bottom panel trace in each case is for
trends on distinct pressure levels. Note that the pressure axis is not linear. The top panel points show MSU layer
equivalent measure trends. MSU layer equivalents have been processed using the method of Thorne et al. (2005). No
attempts have been made to sub-sample to a common data mask.
Differences in trends between available radiosonde datasets are greater during the satellite era than for the
full radiosonde period of record in all regions and at most levels (Figure 2.27, cf. Figure 2.26). The
RAOBCORE product exhibits greater vertical trend gradients than other datasets and it has been posited that
this relates to its dependency upon reanalysis fields (Sakamoto and Christy, 2009; Christy et al., 2010). MSU
trend estimates in the troposphere are generally bracketed by the radiosonde range. In the stratosphere MSU
deep layer estimates tend to show slightly less cooling. Over both 1958–2011 and 1979–2011 there is some
evidence in the radiosonde products taken as a whole that the tropical tropospheric trends increase with
height. But the magnitude and the structure is highly dataset-dependent.
[INSERT FIGURE 2.27 HERE]
Figure 2.27: As Figure 2.26 except for the satellite era 1979–2012 period and including MSU products (RSS, STAR
and UAH).
In summary, based upon multiple independent analyses of measurements from radiosondes and satellite
sensors it is virtually certain that globally the troposphere has warmed and the stratosphere has cooled since
the mid-20th century. Despite unanimous agreement on the sign of the trends, substantial disagreement exists
among available estimates as to the rate of temperature changes, particularly outside the Northern
Hemisphere extra-tropical troposphere, which has been well sampled by radiosondes. Hence there is only
medium confidence in the rate of change and its vertical structure in the Northern Hemisphere extra-tropical
troposphere and low confidence elsewhere.
[START FAQ 2.1 HERE]
FAQ 2.1: How do We Know the World has Warmed?
Evidence for a warming world comes from multiple independent climate indicators, from high up in the
atmosphere to the depths of the oceans. They include changes in surface, atmospheric, and oceanic
temperatures, glaciers, snow cover, sea ice, sea level, and atmospheric water vapour. Scientists from all
over the world have independently verified this evidence many times. That the world has warmed since the
nineteenth century is unequivocal.
Discussion about climate warming often centres on potential residual biases in temperature records from
land-based weather stations. These records are very important, but they only represent one indicator of
changes in the climate system. Broader evidence for a warming world comes from a wide range of
independent physically consistent measurements of many other, strongly interlinked, elements of the climate
system (FAQ 2.1, Figure 1).
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[INSERT FAQ 2.1, FIGURE 1 HERE]
FAQ 2.1, Figure 1: Independent analyses of many components of the climate system that would be expected to change
in a warming world exhibit trends consistent with warming (arrow direction denotes the sign of the change), as shown
in FAQ 2.1, Figure 2.
A rise in global average surface temperatures is the best-known indicator of climate change. Although each
year and even decade is not always warmer than the last, global surface temperatures have warmed
substantially since 1900.
Warming land temperatures correspond closely with the observed warming trend over the oceans. Warming
oceanic air temperatures, measured from aboard ships, and temperatures of the sea surface itself also
coincide, as borne out by many independent analyses.
The atmosphere and ocean are both fluid bodies, so warming at the surface should also be seen in the lower
atmosphere, and deeper down into the upper oceans, and observations confirm that this is indeed the case.
Analyses of measurements made by weather balloon radiosondes and satellites consistently show warming
of the troposphere, the active weather layer of the atmosphere. More than 90% of the excess energy absorbed
by the climate system since at least the 1970s has been stored in the oceans as can be seen from global
records of ocean heat content going back to the 1950s.
As the oceans warm, the water itself expands. This expansion is one of the main drivers of the independently
observed rise in sea levels over the past century. Melting of glaciers and ice sheets also contribute, as do
changes in storage and usage of water on land.
A warmer world is also a moister one, because warmer air can hold more water vapour. Global analyses
show that specific humidity, which measures the amount of water vapour in the atmosphere, has increased
over both the land and the oceans.
The frozen parts of the planet—known collectively as the cryosphere—affect, and are affected by, local
changes in temperature. The amount of ice contained in glaciers globally has been declining every year for
more than 20 years, and the lost mass contributes, in part, to the observed rise in sea level. Snow cover is
sensitive to changes in temperature, particularly during the spring, when snow starts to melt. Spring snow
cover has shrunk across the NH since the 1950s. Substantial losses in Arctic sea ice have been observed
since satellite records began, particularly at the time of the mimimum extent, which occurs in September at
the end of the annual melt season. By contrast, the increase in Antarctic sea ice has been smaller.
Individually, any single analysis might be unconvincing, but analysis of these different indicators and
independent datasets has led many independent research groups to all reach the same conclusion. From the
deep oceans to the top of the troposphere, the evidence of warmer air and oceans, of melting ice and rising
seas all points unequivocally to one thing: the world has warmed since the late 19th century (FAQ 2.1,
Figure 2).
[INSERT FAQ 2.1, FIGURE 2 HERE]
FAQ 2.1, Figure 2: Multiple independent indicators of a changing global climate. Each line represents an
independently-derived estimate of change in the climate element. In each panel all datasets have been normalized to a
common period of record. A full detailing of which source datasets go into which panel is given in the Supplementary
Material 2.SM.5.
[END FAQ 2.1 HERE]
2.5
Changes in Hydrological Cycle
This section covers the main aspects of the hydrological cycle, including large-scale average precipitation,
stream flow and runoff, soil moisture, atmospheric water vapour, and clouds. Meteorological drought is
assessed in Section 2.6. Ocean precipitation changes are assessed in Section 3.4.3 and changes in the area
covered by snow in Section 4.5.
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2.5.1
2.5.1.1
Chapter 2
IPCC WGI Fifth Assessment Report
Large Scale Changes in Precipitation
Global Land Areas
AR4 concluded that precipitation has generally increased over land north of 30°N over the period 1900–2005
but downward trends dominate the tropics since the 1970s. AR4 included analysis of both the GHCN (Vose
et al., 1992) and CRU (Mitchell and Jones, 2005) gauge-based precipitation datasets for the globally
averaged annual precipitation over land. For both datasets the overall linear trend from 1900 to 2005 (1901–
2002 for CRU) was positive but not statistically significant (Table 3.4 from AR4). Other periods covered in
AR4 (1951–2005 and 1979–2005) showed a mix of negative and positive trends depending on the dataset.
Since AR4, existing datasets have been updated and a new dataset developed. Figure 2.28 shows the centuryscale variations and trends on globally and zonally averaged annual precipitation using five datasets: GHCN
V2 (updated through 2011; Vose et al., 1992), Global Precipitation Climatology Project V2.2 (GPCP)
combined raingauge-satellite product (Adler et al., 2003), CRU TS 3.10.01 (updated from Mitchell and
Jones, 2005), Global Precipitation Climatology Centre V6 (GPCC) dataset (Becker et al., 2013) and a
reconstructed dataset by Smith et al. (2012). Each data product incorporates a different number of station
series for each region. The Smith et al. product is a statistical reconstruction using Empirical Orthogonal
Functions, similar to the NCDC MLOST global temperature product (Section 2.4.3) that does provide
coverage for most of the global surface area although only land is included here. The datasets based on in
situ observations only start in 1901, but the Smith et al. dataset ends in 2008, while the other 3 datasets
contain data until 2010.
For the longest common period of record (1901-2008) the GHCN and GPCC datasets show statistically
significant but opposite trends in globally averaged precipitation, with the Smith et al. and CRU datasets
showing small non-significant declines (Table 2.9). Global trends for the shorter period (1951–2008) also
show a mix of statistically non-significant positive and negative trends amongst the three datasets. These
differences among datasets, particularly for the early part of the 20th century indicate that long-term
increases in global precipitation discussed in AR4 are uncertain, owing to issues in data coverage in the early
part of the 20th century (Wan et al., 2013).
[INSERT FIGURE 2.28 HERE]
Figure 2.28: Annual precipitation anomalies averaged over land areas for four latitudinal bands and the globe from five
global precipitation datasets relative to a 1981–2000 climatology.
Table 2.9: Trend estimates and 90% confidence intervals (Box 2.2) for annual precipitation for each time series in
Figure 2.28 over two common periods of record.
Trends in mm yr–1 per decade
Dataset
Area
1901–2008
1951–2008
CRU TS 3.10.01 (updated from Mitchell and Jones, 2005)
Global
–0.337 ± 1.670
2.036 ± 4.260
GHCN V2 (updated through 2011; Vose et al., 1992)
Global
2.081 ± 1.662
–2.767 ± 3.919
GPCC V6 (Becker et al., 2013)
Global
–2.610 ± 1.497
–1.861 ± 4.010
Smith et al. (2012)
Global
–0.392 ± 1.497
1.792 ± 3.707
In summary, confidence in precipitation change averaged over global land areas is low prior to 1950 and
medium afterwards because of insufficient data, particularly in the earlier part of the record. Available
globally incomplete records show mixed and nonsignificant long-term trends in reported global mean
changes. Further, when virtually all the land area is filled in using a reconstruction method, the resulting time
series shows little change in land-based precipitation since 1900.
2.5.1.2
Spatial Variability of Observed Trends
The latitude band plots in Figure 2.28 suggest that precipitation over tropical land areas (30°S–30°N) has
increased over the last decade reversing the drying trend that occurred from the mid-1970s to mid-1990s. As
a result the period 1951–2008 shows no significant overall trend in tropical land precipition (Table 2.10).
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Longer term trends (1901–2008) in the tropics, shown in Table 2.10, are also non-significant for each of the
four datasets. The mid-latitudes of the NH (30°N–60°N) show an overall increase in precipitation from 1901
to 2008 with statistically significant trends for each dataset. For the shorter period (1951–2008) the trends
are also positive but non-significant for two of the four datasets. For the high latitudes of the NH (60°N–
90°N) the GHCN dataset shows a significant increase for the 1951–2008 period, whereas the other three
datasets show no significant change (Table 2.10). Fewer data from high latitude stations make these trends
less certain. Because of insufficient data, longer trends for the high latitudes are not available. In the midlatitudes of the SH (60°S–30°S) there is evidence of long-term increases with three datasets showing
significant trends for the 1901–2008 period. For the 1951–2008 period changes in SH mid-latitude
precipitation are less certain with only one dataset showing a significant trend towards drying. All datasets
show an abrupt decline in SH mid-latitude precipitation in the early 2000s (Figure 2.28) consistent with
enhanced drying that has very recently recovered. These results for latitudinal changes are consistent with
the global satellite observations for the 1979–2008 period (Allan et al., 2010) and land-based gauge
measurements for the 1950–1999 period (Zhang et al., 2007a) which both show that precipitation has
increased over wet regions of the tropics and NH mid-latitudes, and decreased over dry regions of the
subtropics.
Table 2.10: Trend estimates and 90% confidence intervals (Box 2.2) for annual precipitation for each time series in
Figure 2.28 over two periods. Dashes indicate not enough data available for trend calculation. For the latitudinal band
90°S–60°S not enough data exists for each product in either period.
Trends in mm yr–1 per decade
Dataset
Area
1901–2008
1951–2008
CRU TS 3.10.01 (updated from Mitchell and Jones, 2005) 60°N–90°N
–0.188 ± 0.405
30°N–60°N
2.770 ± 1.906
5.652 ± 3.725
30°S–30°N
–2.010 ± 3.729
–9.528 ± 10.872
60°S–30°S
2.964 ± 2.853
–2.747 ± 7.962
GHCN V2 (updated through 2011; Vose et al., 1992)
60°N–90°N
4.518 ± 2.640
30°N–60°N
3.234 ± 1.097
1.385 ± 1.980
30°S–30°N
1.014 ± 3.002
–5.147 ± 7.275
60°S–30°S
–0.565 ± 2.265
–8.009 ± 5.632
GPCC V6 (Becker et al., 2013)
60°N–90°N
–0.029 ± 0.424
30°N–60°N
3.135 ± 1.049
1.500 ± 1.926
30°S–30°N
–0.477 ± 3.348
–4.162 ± 9.646
60°S–30°S
2.403 ± 2.006
–0.510 ± 5.451
Smith et al. (2012)
60°N–90°N
0.305 ± 0.487
30°N–60°N
1.444 ± 0.502
0.969 ± 0.881
30°S–30°N
0.429 ± 1.476
0.665 ± 4.753
60°S–30°S
2.944 ± 1.397
0.784 ± 3.310
In AR4, maps of observed trends of annual precipitation for 1901–2005 were calculated using GHCN
interpolated to a 5° × 5° latitude/longitude grid. Trends (in % per decade) were calculated for each grid box
and showed statistically significant changes, particularly increases in eastern and northwestern North
America, parts of Europe and Russia, southern South America, and Australia, declines in the Sahel region of
Africa, and a few scattered declines elsewhere.
Figure 2.29 shows the spatial variability of long-term trends (1901–2010) and more recent trends (1979–
2010) over land in annual precipitation using the CRU, GHCN, and GPCC datasets. The trends are computed
from land-only grid box time series using each native dataset grid resolution. The patterns of these absolute
trends (in mm yr–1 per decade) are broadly similar to the trends (in % per decade) relative to local
climatology (Supplementary Material 2.SM.6.1). Increases for the period 1901–2010 are seen in the midand higher-latitudes of both the NH and SH consistent with the reported changes for latitudinal bands. At the
grid box scale, statistically significant trends occur in most of the same areas, in each dataset. The GPCC
map shows the most areas with significant trends. Comparing the maps in Figure 2.29, many of the areas that
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showed statistically significant long-term trends in AR4 show opposite trends between the 1901–2010 period
and the 1979–2010 period (e.g., parts of North America including the western USA, parts of southern South
America, southern Africa, northeastern Africa and Spain, Iceland, and Russia). The prominent exception is
the Sahel region which for 1979–2010 continues to show an increase, although not as strong as reported in
AR4.
[INSERT FIGURE 2.29 HERE]
Figure 2.29: Trends in precipitation over land from the CRU, GHCN and GPCC datasets for 1901–2010 (left hand
panels) and 1979–2010 (right hand panels). Trends have been calculated only for those grid boxes with greater than
70% complete records and more than 20% data availability in first and last decile of the period. White areas indicate
incomplete or missing data. Black plus signs (+) indicate grid boxes where trends are significant (i.e., a trend of zero
lies outside the 90% confidence interval).
In summary, the land areas of the mid-latitudes of the NH show a likely overall increase in precipitation
(medium confidence since 1901, but high confidence after 1951). Since 1951, precipitation in the highlatitudes of the NH also shows increases, but confidence is low for the changes in this region. There is
evidence for increased precipitation in the mid-latitudes of the SH since 1901 (medium confidence). It is
likely there was an abrupt decline in SH mid-latitude precipitation in the early 2000s consistent with
enhanced drying that has very recently recovered. Precipitation in the tropical land areas has increased
(medium confidence) over the last decade, reversing the drying trend that occurred from the mid-1970s to
mid-1990s reported in AR4. Consequently, there is little evidence for longer term changes in tropical
precipitation over land.
2.5.1.3
Changes in Snowfall
AR4 draws no conclusion on global changes in snowfall. Changes in snowfall are discussed on a region-byregion basis, but mainly focussed on North America and Eurasia.Statistically significant increases were
found in most of Canada, parts of northern Europe and Russia. A number of areas showed a decline in the
number of snowfall events, especially those where climatological averaged temperatures were close to 0°C
and where warming led to earlier onset of spring. Also, an increase in lake-effect snowfall was found for
areas near the North American Great Lakes.
Since AR4, most published literature has considered again changes in snowfall in North America. These
studies have confirmed that more winter-time precipitation is falling as rain rather than snow in the Western
United States (Knowles et al., 2006), the Pacific Northwest and Central United States (Feng and Hu, 2007).
Kunkel et al. (2009) analyzed trends using a specially quality-controlled dataset of snowfall observations
over the contiguous U.S. and found that snowfall has been declining in the Western U.S., Northeastern U.S.
and southern margins of the seasonal snow region, but increasing in the western Great Plains and Great
Lakes regions. Snowfall in Canada has increased mainly in the north while a significant decrease was
observed in the southwestern part of the country for 1950–2009 (Mekis and Vincent, 2011).
Other regions that have been analyzed include Japan (Takeuchi et al., 2008), where warmer winters in the
heavy snowfall areas on Honshu are associated with decreases in snowfall and precipitation in general.
Shekar et al. (2010) found declines in total seasonal snowfall along with increases in maximum and
minimum temperatures in the western Himalaya. Serquet et al. (2011) analyzed snowfall and rainfall days
since 1961 and found the proportion of snowfall days to rainfall days in Switzerland was declining in
association with increasing temperatures. Scherrer and Appenzeller (2006) found a trend in a pattern of
variability of snowfall in the Swiss Alps that indicated decreasing snow at low altitudes relative to high
altitudes, but with large decadal variability in key snow indicators (Scherrer et al., 2013). Van Ommen and
Morgan (2010) draw a link between increased snowfall in coastal East Antarctica and increased southwest
Western Australia drought. However, Monaghan and Bromwich (2008) found an increase in snow
accumulation over all Antarctica from the late 1950s to 1990, then a decline to 2004. Thus snowfall changes
in Antarctica remain uncertain.
In summary, in most regions analyzed, it is likely that decreasing numbers of snowfall events are occurring
where increased winter temperatures have been observed (North America, Europe, Southern and Eastern
Asia). Confidence is low for the changes in snowfall over Antarctica.
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2.5.2
Chapter 2
IPCC WGI Fifth Assessment Report
Streamflow and Runoff
AR4 concluded that runoff and river discharge generally increased at high latitudes, with some exceptions.
No consistent long-term trend in discharge was reported for the world’s major rivers on a global scale.
River discharge is unique among water cycle components in that it both spatially and temporally integrates
surplus waters upstream within a catchment (Shiklomanov et al., 2010), which makes it well suited for in situ
monitoring (Arndt et al., 2010). The most recent comprehensive analyses (Milliman et al., 2008; Dai et al.,
2009) do not support earlier work (Labat et al., 2004) that reported an increasing trend in global river
discharge associated with global warming during the 20th century. It must be noted that many if not most
large rivers, especially those for which a long-term streamflow record exists, have been impacted by human
influences such as dam construction or land use, so results must be interpreted with caution. Dai et al. (2009)
assembled a dataset of 925 most downstream stations on the largest rivers monitoring 80% of the global
ocean draining land areas and capturing 73% of the continental runoff. They found that discharges in about
one-third of the 200 largest rivers (including the Congo, Mississippi, Yenisey, Paraná, Ganges, Columbia,
Uruguay, and Niger) show statistically significant trends during 1948–2004, with the rivers having
downward trends (45) outnumbering those with upward trends (19). Decreases in streamflow were found
over many low and mid-latitude river basins such as the Yellow River in northern China since 1960s (Piao et
al., 2010) where precipitation has decreased. Increases in streamflow during the latter half of the 20th
century also have been reported over regions with increased precipitation, such as parts of the United States
(Groisman et al., 2004), and in the Yangtze River in southern China (Piao et al., 2010). In the Amazon basin
an increase of discharge extremes is observed over recent decades (Espinoza Villar et al., 2009). For France,
Giuntoli et al. (2013) found that the sign of the temporal trends in natural streamflows varies with period
studied. In that case study, significant correlations between median to low flows and the Atlantic
Multidecadal Oscillation (AMO; Section 2.7.8) result in long quasi-periodic oscillations.
At high latitudes, increasing winter base flow and mean annual stream flow resulting from possible
permafrost thawing were reported in Northwest Canada (St. Jacques and Sauchyn, 2009). Rising minimum
daily flows also have been observed in northern Eurasian rivers (Smith et al., 2007). For ocean basins other
than the Arctic, and for the global ocean as a whole, the data for continental discharge show small or
downward trends, which are statistically significant for the Pacific (–9.4 km3 yr–1). Precipitation is a major
driver for the discharge trends and for the large interannual-to-decadal variations (Dai et al., 2009).
However, for the Arctic drainage areas, Adam and Lettenmaier (2008) found that upward trends in
streamflow are not accompanied by increasing precipitation, especially over Siberia, based on available
observations. Zhang et al. (2012a) argued that precipitation measurements are sparse and exhibit large coldseason biases in the Arctic drainage areas and hence there would be large uncertianties using these data to
investigate their influence on streamflow.
Recently, Stahl et al. (2010) and Stahl and Tallaksen (2012) investigated streamflow trends based on a
dataset of near-natural streamflow records from more than 400 small catchments in 15 countries across
Europe for 1962–2004. A regional coherent pattern of annual streamflow trends was revealed with negative
trends in southern and eastern regions, and generally positive trends elsewhere. Subtle regional differences in
the subannual changes in various streamflow metrics also can be captured in regional studies such as by
Monk et al. (2011) for Canadian Rivers.
In summary, the most recent comprehensive analyses lead to the conclusion that confidence is low for an
increasing trend in global river discharge during the 20th century.
2.5.3
Evapotranspiration Including Pan Evaporation
AR4 concluded that decreasing trends were found in records of pan evaporation over recent decades over the
USA, India, Australia, New Zealand, China and Thailand and speculated on the causes including decreased
surface solar radiation, sunshine duration, increased specific humidity and increased clouds. However, AR4
also reported that direct measurements of evapotranspiration over global land areas are scarce, and
concluded that reanalysis evaporation fields are not reliable because they are not well constrained by
precipitation and radiation.
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Since AR4 gridded datasets have been developed that estimate actual evapotranspiration from either
atmospheric forcing and thermal remote sensing, sometimes in combination with direct measurements (e.g.,
from FLUXNET, a global network of flux towers), or interpolation of FLUXNET data using regression
techniques, providing an unprecedented look at global evapotranspiration (Mueller et al., 2011). On a global
scale, evapotranspiration over land increased from the early 1980s up to the late 1990s (Wild et al., 2008;
Jung et al., 2010; Wang et al., 2010) and Wang et al. (2010) found that global evapotranspiration increased at
a rate of 0.6 W m–2 per decade for the period 1982–2002. After 1998, a lack of moisture availability in SH
land areas, particularly decreasing soil moisture, has acted as a constraint to further increase of global
evapotranspiration (Jung et al., 2010).
Zhang et al. (2007b) found decreasing pan evaporation at stations across the Tibetan Plateau, even with
increasing air temperature. Similarly, decreases in pan evaporation were also found for northeastern India
(Jhajharia et al., 2009) and the Canadian Prairies (Burn and Hesch, 2007). A continuous decrease in
reference and pan evaporation for the period 1960–2000 was reported by Xu et al. (2006a) for a humid
region in China, consistent with reported continuous increase in aerosol levels over China (Qian et al., 2006;
Section 2.2.4). Roderick et al. (2007) examined the relationship between pan evaporation changes and many
of the possible causes listed above using a physical model and conclude that many of the decreases (USA,
China, Tibetian Plateau, Australia) cited above are related to declining wind speeds and to a lesser extent
decreasing solar radiation. Fu et al. (2009) provided an overview of pan evaporation trends and concluded
the major possible causes, changes in wind speed, humidity, and solar radiation, have been occurring, but
that the importance of each is regionally dependent.
The recent increase in incoming shortwave radiation in regions with decreasing aerosol concentrations
(Section 2.2.3) can explain positive evapotranspiration trends only in the humid part of Europe. In semi-arid
and arid regions, trends in evapotranspiration largely follow trends in precipitation (Jung et al., 2010).
Trends in surface winds (Section 2.7.2) and CO2 (Section 2.2.1.1.1) also alter the partitioning of available
energy into evapotranspiration and sensible heat. While surface wind trends may explain pan evaporation
trends over Australia (Rayner, 2007; Roderick et al., 2007), their impact on actual evapotranspiration is
limited due to the compensating effect of boundary-layer feedbacks (van Heerwaarden et al., 2010). In
vegetated regions, where a large part of evapotranspiration comes from transpiration through plants’ stomata,
rising CO2 concentrations can lead to reduced stomatal opening and evapotranspiration (Idso and Brazel,
1984; Leakey et al., 2006). Additional regional effects that impact evapotranspiration trends are lengthening
of the growing season and land use change.
In summary, there is medium confidence that pan evaporation continued to decline in most regions studied
since AR4 related to changes in wind speed, solar radiation and humidity. On a global scale,
evapotranspiration over land increased (medium confidence) from the early 1980s up to the late 1990s. After
1998, a lack of moisture availability in SH land areas, particularly decreasing soil moisture, has acted as a
constraint to further increase of global evapotranspiration.
2.5.4
Surface Humidity
AR4 reported widespread increases in surface air moisture content since 1976, along with near-constant
relative humidity over large scales though with some significant changes specific to region, time of day or
season.
In good agreement with previous analysis from Dai (2006), Willett et al. (2008) show widespread increasing
specific humidity across the globe from the homogenised gridded monthly mean anomaly product
HadCRUH (1973–2003). Both Dai and HadCRUH products that are blended land and ocean data products
end in 2003 but HadISDH (1973–2012) (Willett et al., 2013) and the NOCS product (Berry and Kent, 2009)
are available over the land and ocean respectively through 2012. There are some small isolated but coherent
areas of drying over some of the more arid land regions (Figure 2.30a). Moistening is largest in the tropics
(Table 2.11) and the summer hemisphere over both land and ocean. Large uncertainty remains over the SH
where data are sparse. Global specific humidity is sensitive to large scale phenomena such as ENSO (Figure
2.30b; Box 2.5). It is strongly correlated with land surface temperature averages over the 23 Giorgi and
Francisco (2000) regions for the period 1973–1999 and mostly exhibits increases at or above the increase
expected from the Clausius-Clapeyron relation (about 7% °C–1; Annex III: Glossary) with high confidence
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(Willett et al., 2010). Land surface humidity trends are similar in ERA-Interim to observed estimates of
homogeneity-adjusted datasets (Simmons et al., 2010; Figure 2.30b).
[INSERT FIGURE 2.30 HERE]
Figure 2.30: a) Trends in surface specific humidity from HadISDH and NOCS over 1973–2012. Trends have been
calculated only for those grid boxes with greater than 70% complete records and more than 20% data availability in first
and last decile of the period. White areas indicate incomplete or missing data. Black plus signs (+) indicate grid boxes
where trends are significant (i.e., a trend of zero lies outside the 90% confidence interval). b) Global annual average
anomalies in land surface specific humidity from Dai (2006; red), HadCRUH (Willett et al., 2013; orange), HadISDH
(Willett et al., 2013; black), and ERA-Interim (Simmons et al., 2010; blue). Anomalies are relative to the 1979-2003
climatology.
Table 2.11: Trend estimates and 90% confidence intervals (Box 2.2) for surface humidity over two periods.
Trends in % per decade
Dataset
1976–2003
1973–2012
Land
HadISDH (Willett et al., 2008)
0.127 ± 0.037
0.091 ± 0.023
HadCRUH_land (Willett et al., 2008)
0.128 ± 0.043
Dai_land (Dai, 2006)
0.099 ± 0.046
Ocean
NOCS (Berry and Kent, 2009)
0.114 ± 0.064
0.090 ± 0.033
HadCRUH_marine (Willett et al., 2008) 0.065 ± 0.049
Dai_marine (Dai, 2006)
0.058 ± 0.044
Since 2000 surface specific humidity over land has remained largely unchanged (Figure 2.30) whereas land
areas have on average warmed slightly (Figure 2.14) implying a reduction in land region relative humidity.
This may be linked to the greater warming of the land surface relative to the ocean surface (Joshi et al.,
2008). The marine specific humidity (Berry and Kent, 2009), like that over land, shows widespread increases
that correlate strongly with SST. However, there is a marked decline in marine relative humidity around
1982. This is reported in Willett et al. (2008) where its origin is concluded to be a non-climatic data issue
owing to a change in reporting practice for dewpoint temperature.
In summary, it is very likely that global near surface air specific humidity has increased since the 1970s.
However, during recent years the near surface moistening over land has abated (medium confidence). As a
result, fairly widespread decreases in relative humidity near the surface are observed over the land in recent
years.
2.5.5
Tropospheric Humidity
As reported in AR4, observations from radiosonde and GPS measurements over land, and satellite
measurements over ocean indicate increases in tropospheric water vapour at near-global spatial scales which
are consistent with the observed increase in atmospheric temperature over the last several decades.
Tropospheric water vapour plays an important role in regulating the energy balance of the surface and topof-atmosphere, provides a key feedback mechanism and is essential to the formation of clouds and
precipitation.
2.5.5.1
Radiosonde
Radiosonde humidity data for the troposphere were used sparingly in AR4, noting a renewed appreciation for
biases with the operational radiosonde data that had been highlighted by several major field campaigns and
intercomparisons. Since AR4 there have been three distinct efforts to homogenize the tropospheric humidity
records from operational radiosonde measurements (Durre et al., 2009; McCarthy et al., 2009; Dai et al.,
2011) (Supplementary Material 2.SM.6.1, Table 2.SM.9). Over the common period of record from 1973
onwards, the resulting estimates are in substantive agreement regarding specific humidity trends at the
largest geographical scales. On average, the impact of the correction procedures is to remove an artificial
temporal trend towards drying in the raw data and indicate a positive trend in free tropospheric specific
humidity over the period of record. In each analysis, the rate of increase in the free troposphere is concluded
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to be largely consistent with that expected from the Clausius-Clapeyron relation (about 7%/°C). There is no
evidence for a significant change in free tropospheric relative humidity, although a decrease in relative
humidity at lower levels is observed (Section 2.5.5). Indeed, McCarthy et al. (2009) show close agreement
between their radiosonde product at the lowest levels and HadCRUH (Willett et al., 2008).
2.5.5.2
GPS
Since the early 1990s, estimates of column integrated water vapour have been obtained from ground-based
Global Positioning System (GPS) receivers. An international network started with about 100 stations in 1997
and has currently been expanded to over 500 (primarily land-based) stations. Several studies have compiled
GPS water vapour datasets for climate studies (Jin et al., 2007; Wang et al., 2007; Wang and Zhang, 2008,
2009). Using such data, Mears et al. (2010) demonstrated general agreement of the interannual anomalies
between ocean-based satellite and land-based GPS column integrated water vapour data. The interannual
water vapour anomalies are closely tied to the atmospheric temperature changes in a manner consistent with
that expected from the Clausius-Clapeyron relation. Jin et al. (2007) found an average column integrated
water vapour trend of about 2 kg m–2 per decade during 1994–2006 for 150 (primarily land-based) stations
over the globe, with positive trends at most NH stations and negative trends in the SH. However, given the
short length (about 10 years) of the GPS records, the estimated trends are very sensitive to the start and end
years and the analyzed time period (Box 2.2).
2.5.5.3
Satellite
AR4 reported positive decadal trends in lower and upper tropospheric water vapour based upon satellite
observations for the period 1988–2004. Since AR4, there has been continued evidence for increases in lower
tropospheric water vapour from microwave satellite measurements of column integrated water vapour over
oceans (Santer et al., 2007; Wentz et al., 2007) and globally from satellite measurements of spectrallyresolved reflected solar radiation (Mieruch et al., 2008). The interannual variability and longer-term trends in
column-integrated water vapour over oceans are closely tied to changes in SST at the global scale and
interannual anomalies show remarkable agreement with low level specific humidity anomalies from
HadCRUH (O'Gorman et al., 2012). The rate of moistening at large spatial scales over oceans is close to that
expected from the Clausius-Clapeyron relation (about 7% per °C) with invariant relative humidity (Figure
2.31). Satellite measurements also indicate that the globally-averaged upper tropospheric relative humidity
has changed little over the period 1979–2010 while the troposphere has warmed, implying an increase in the
mean water vapour mass in the upper troposphere (Shi and Bates, 2011).
[INSERT FIGURE 2.31 HERE]
Figure 2.31: a) Trends in column integrated water vapour over ocean surfaces from Special Sensor Microwave Imager,
(Wentz et al., 2007) for the period 1988–2010. Trends have been calculated only for those grid boxes with greater than
70% complete records and more than 20% data availability in first and last decile of the period. Black plus signs (+)
indicate grid boxes where trends are significant (i.e., a trend of zero lies outside the 90% confidence interval). b) Global
annual average anomalies in column integrated water vapour averaged over ocean surfaces. Anomalies are relative to
the 1988–2007 average.
Interannual variations in temperature and upper tropospheric water vapour from infrared satellite data are
consistent with a constant RH behavior at large spatial scales (Dessler et al., 2008; Gettelman and Fu, 2008;
Chung et al., 2010). On decadal time-scales, increased greenhouse gas concentrations reduce clear-sky
outgoing long-wave radiation (Allan, 2009; Chung and Soden, 2010), thereby influencing inferred
relationships between moisture and temperature. Using Meteosat infrared radiances, Brogniez et al. (2009)
demonstrated that interannual variations in free tropospheric humidity over subtropical dry regions are
heavily influenced by meridional mixing between the deep tropics and the extra tropics. Regionally, upper
tropospheric humidity changes in the tropics were shown to relate strongly to the movement of the ITCZ
based upon microwave satellite data (Xavier et al., 2010). Shi and Bates (2011) found an increase in upper
tropospheric humidity over the equatorial tropics from 1979 to 2008. However there was no significant trend
found in tropical-mean or global-mean averages, indicating that on these time and space scales the upper
troposphere has seen little change in relative humidity over the past 30 years. While microwave satellite
measurements have become increasingly relied upon for studies of upper tropospheric humidity, the absence
of a homogenized dataset across multiple satellite platforms presents some difficulty in documenting
coherent trends from these records (John et al., 2011).
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2.5.5.4
Chapter 2
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Reanalyses.
Using NCEP reanalyses for the period 1973–2007, Paltridge et al. (2009) found negative trends in specific
humidity above 850 hPa over both the tropics and southern midlatitudes, and above 600 hPa in the NH
midlatitudes. However, as noted in AR4, reanalysis products suffer from time dependent biases and have
been shown to simulate unrealistic trends and variability over the ocean (Mears et al., 2007; John et al.,
2009) (Box 2.3). Some reanalysis products do reproduce observed variability in low level humidity over land
(Simmons et al., 2010), more complete assesments of multiple reanalysis products yield substantially
different and even opposing trends in free tropospheric specific humidity (Chen et al., 2008; Dessler and
Davis, 2010). Consequently, reanalysis products are still considered to be unsuitable for the analysis of
tropospheric water vapour trends (Sherwood et al., 2010).
In summary, radiosonde, GPS and satellite observations of tropospheric water vapour indicate very likely
increases at near global scales since the 1970s occurring at a rate that is generally consistent with the
Clausius-Clapeyron relation (about 7% °C–1) and the observed increase in atmospheric temperature.
Significant trends in tropospheric relative humidity at large spatial scales have not been observed, with the
exception of near-surface air over land where relative humidity has decreased in recent years (Section 2.5.5).
2.5.6
2.5.6.1
Clouds
Surface Observations
AR4 reported that surface-observed total cloud cover may have increased over many land areas since the
middle of the 20th century, including the USA, the former USSR, Western Europe, midlatitude Canada, and
Australia. A few regions exhibited decreases, including China and central Europe. Trends were less globally
consistent since the early 1970s, with regional reductions in cloud cover were reported for western Asia and
Europe but increases over the USA.
Analyses since AR4 have indicated decreases in cloud occurrence/cover in recent decades over Poland
(Wibig, 2008), China and Tibet (Duan and Wu, 2006; Endo and Yasunari, 2006; Xia, 2010b), in particular
for upper level clouds (Warren et al., 2007) and also over Africa, Eurasia and in particular South America
(Warren et al., 2007). Increased frequency of overcast conditions has been reported for some regions, such as
Canada from 1953 to 2002 (Milewska, 2004), with no statistically significant trends evident over Australia
(Jovanovic et al., 2011) and North America (Warren et al., 2007). A global analysis of surface observations
spanning the period 1971–2009 (Eastman and Warren, 2012) indicates a small decline in total cloud cover of
~0.4% per decade which is largely attributed to declining mid- and high-level cloud cover and is most
prominent in the middle latitudes.
Regional variability in surface-observed cloudiness over the ocean appeared more credible than zonal and
global mean variations in AR4. Multidecadal changes in upper-level cloud cover and total cloud cover over
particular areas of the tropical Indo-Pacific Ocean were consistent with island precipitation records and SST
variability. This has been extended more recently by Deser et al. (2010a), who found that an eastward shift in
tropical convection and total cloud cover from the western to central equatorial Pacific occurred over the
20th century and attributed it to a long-term weakening of the Walker circulation (Section 2.7.5). Eastman et
al. (2011) report that, after the removal of apparently spurious globally coherent variability, cloud cover
decreased in all subtropical stratocumulus regions from 1954 to 2008.
2.5.6.2
Satellite Observations
Satellite cloud observations offer the advantage of much better spatial and temporal coverage compared to
surface observations. However they require careful efforts to identify and correct for temporal discontinuities
in the datasets associated with orbital drift, sensor degradation, and inter-satellite calibration differences.
AR4 noted that there were substantial uncertainties in decadal trends of cloud cover in all satellite datasets
available at the time and concluded that there was no clear consensus regarding the decadal changes in total
cloud cover. Since AR4 there has been continued effort to assess the quality of and develop improvements to
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multi-decadal cloud products from operational satellite platforms (Evan et al., 2007; O'Dell et al., 2008;
Heidinger and Pavolonis, 2009).
Several satellite datasets offer multi-decadal records of cloud cover (Stubenrauch et al., 2013). AR4 noted
that there were discrepancies in global cloud cover trends between ISCCP and other satellite data products,
notably a large downward trend of global cloudiness in ISCCP since the late 1980s, which is inconsistent
with PATMOS-x and surface observations (Baringer et al., 2010). Recent work has confirmed the conclusion
of AR4, that much of the downward trend in ISCCP is spurious and an artefact of changes in satellite
viewing geometry (Evan et al., 2007). An assesment of long-term variations in global-mean cloud amount
from nine different satellite datasets by Stubenrauch et al. (2013) found differences between datasets were
comparable in magnitude to the interannual variability (2.5–3.5%). Such inconsistencies result from
differnces in sampling as well as changes in instrument calibration and inhibit an accurate assessment of
global-scale cloud cover trends.
Satellite observations of low-level marine clouds suggest no long-term trends in cloud liquid water path or
optical properties (O'Dell et al., 2008; Rausch et al., 2010). On regional scales, trends in cloud properties
over China have been linked to changes in aerosol concentrations (Qian et al., 2009; Bennartz et al., 2011)
(Section 2.2.3).
In summary, surface-based observations show region- and height-specific variations and trends in cloudiness
but there remains substantial ambiguity regarding global-scale cloud variations and trends, especially from
satellite observations. While trends of cloud cover are consistent between independent datasets in certain
regions, substantial ambiguity and therefore low confidence remains in the observations of global-scale cloud
variability and trends.
2.6
Changes in Extreme Events
AR4 highlighted the importance of understanding changes in extreme climate events (Annex III: Glossary)
because of their disproportionate impact on society and ecosystems compared to changes in mean climate
(see also IPCC Working Group II). More recently a comprehensive assessment of observed changes in
extreme events was undertaken by the IPCC Special Report on Managing the Risks of Extreme Events and
Disasters to Advance Climate Change Adaptation (SREX) (Seneviratne et al., 2012; Section 1.3.3).
Data availability, quality and consistency especially affect the statistics of extremes and some variables are
particularly sensitive to changing measurement practices over time. For example, historical tropical cyclone
records are known to be heterogeneous due to changing observing technology and reporting protocols
(Section 14.6.1) and when records from multiple ocean basins are combined to explore global trends,
because data quality and reporting protocols vary substantially between regions (Knapp and Kruk, 2010).
Similar problems have been discovered when analysing wind extremes, because of the sensitivity of
measurements to changing instrumentation and observing practice (e.g., Smits et al., 2005; Wan et al., 2010).
Numerous regional studies indicate that changes observed in the frequency of extremes can be explained or
inferred by shifts in the overall probability distribution of the climate variable (Griffiths et al., 2005;
Ballester et al., 2010; Simolo et al., 2011). However, it should be noted that these studies refer to counts of
threshold exceedance—frequency, duration—which closely follow mean changes. Departures from high
percentiles/return periods (intensity, severity, magnitude) are highly sensitive to changes in the shape and
scale parameters of the distribution (Schar et al., 2004; Clark et al., 2006; Della-Marta et al., 2007a; DellaMarta et al., 2007b; Fischer and Schar, 2010) and geographical location. Debate continues over whether
variance as well as mean changes are affecting global temperature extremes (Hansen et al., 2012; Rhines and
Huybers, 2013) as illustrated in Figure 1.8 and FAQ 2.2, Figure 1. In the following sections the conclusions
from both AR4 and SREX are reviewed along with studies subsequent to those assessments.
2.6.1
Temperature Extremes
AR4 concluded that it was very likely that a large majority of global land areas had experienced decreases in
indices of cold extremes and increases in indices of warm extremes, since the middle of the 20th century,
consistent with warming of the climate. In addition, globally averaged multi-day heat events had likely
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exhibited increases over a similar period. SREX updated AR4 but came to similar conclusions while using
the revised AR5 uncertainty guidance (Seneviratne et al., 2012). Further evidence since then indicates that
the level of confidence that the majority of warm and cool extremes show warming remains high.
A large amount of evidence continues to support the conclusion that most global land areas analysed have
experienced significant warming of both maximum and minimum temperature extremes since about 1950
(Donat et al., 2013c). Changes in the occurrence of cold and warm days (based on daily maximum
temperatures) are generally less marked (Figure 2.32). The ENSO (Box 2.5) influences both maximum and
minimum temperature variability especially around the Pacific Rim (e.g., Kenyon and Hegerl, 2008;
Alexander et al., 2009) but often affecting cold and warm extremes differently. Different datasets using
different gridding methods and/or input data (Supplementary Material 2.SM.7) indicate large coherent trends
in temperature extremes globally, associated with warming (Figure 2.32). The level of quality control varies
between these datasets. For example, HadEX2 (Donat et al., 2013c) uses more rigorous quality control which
leads to a reduced station sample compared to GHCNDEX (Donat et al., 2013a) or HadGHCND (Caesar et
al., 2006). However despite these issues datasets compare remarkably consistently even though the station
networks vary through time (Figure 2.32; Table 2.12). Other datasets that have assessed these indices, but
cover a shorter period, also agree very well over the period of overlapping data, e.g., HadEX (Alexander et
al., 2006) and Duke (Morak et al., 2011; Morak et al., 2013).
Table 2.12: Trend estimates and 90% confidence intervals (Box 2.2) for global values of cold nights (TN10p), cold
days (TX10p), warm nights (TN90p) and warm days (TX90p) over the periods 1951–2010 and 1979–2010 (see Box
2.4, Table 1 for more information on indices).
Trends in days per decade
Dataset
TN10p
TX10p
TN90p
TX90p
1951–2010 1979–2010 1951–2010 1979–2010 1951–2010 1979–2010 1951–2010 1979–2010
HadEX2 (Donat et –3.9 ± 0.6 –4.2 ± 1.2 –2.5 ± 0.7 –4.1 ± 1.4 4.5 ± 0.9 6.8 ± 1.8
2.9 ± 1.2
6.3 ± 2.2
al., 2013c)
HadGHCND
–4.5 ± 0.7 –4.0 ± 1.5 –3.3 ± 0.8 –5.0 ± 1.6 5.8 ± 1.3 8.6 ± 2.3
4.2 ± 1.8
9.4 ± 2.7
(Caesar et al.,
2006)
GHCNDEX
–3.9 ± 0.6 –3.9 ± 1.3 –2.6 ± 0.7 –3.9 ± 1.4 4.3 ± 0.9 6.3 ± 1.8
2.9 ± 1.2
6.1 ± 2.2
(Donat et al.,
2013a)
The shift in the distribution of night-time temperatures appears greater than daytime temperatures although
whether distribution changes are simply linked to increases in the mean or other moments is an active area of
research (Ballester et al., 2010; Simolo et al., 2011; Donat and Alexander, 2012; Hansen et al., 2012).
Indeed, all datasets examined (Duke, GHCNDEX, HadEX, HadEX2 and HadGHCND), indicate a faster
increase in minimum temperature extremes than maximum temperature extremes. While diurnal temperature
range (DTR) declines have only been assessed with medium confidence (Section 2.4.1.2), confidence of
accelerated increases in minimum temperature extremes compared to maximum temperature extremes is
high due to the more consistent patterns of warming in minimum temperature extremes globally.
Regional changes in a range of climate indices are assessed in Table 2.13. These indicate likely increases
across most continents in unusually warm days and nights and/or reductions in unusually cold days and
nights including frosts. Some regions have experienced close to a doubling of the occurrence of warm and a
halving of the occurrence of cold nights e.g., parts of the Asia-Pacific region (Choi et al., 2009) and parts of
Eurasia (Klein Tank et al., 2006; Donat et al., 2013a; Donat et al., 2013c) since the mid-20th century.
Changes in both local and global SST patterns (Section 2.4.2) and large scale circulation patterns (Section
2.7) have been shown to be associated with regional changes in temperature extremes (Barrucand et al.,
2008; Scaife et al., 2008; Alexander et al., 2009; Li et al., 2012), particularly in regions around the Pacific
Rim (Kenyon and Hegerl, 2008). Globally, there is evidence of large-scale warming trends in the extremes
of temperature, especially minimum temperature, since the beginning of the 20th century (Donat et al.,
2013c).
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[INSERT TABLE 2.13 HERE]
Table 2.13: Regional observed changes in a range of climate indices since the middle of the 20th century. Assessments
are based on a range of ‘global’ studies and assessments (Groisman et al., 2005; Alexander et al., 2006; Caesar et al.,
2006; Sheffield and Wood, 2008; Dai, 2011a; Dai, 2011b; Seneviratne et al., 2012; Sheffield et al., 2012; Dai, 2013;
Donat et al., 2013a; Donat et al., 2013c; van der Schrier et al., 2013) and selected regional studies as indicated. Bold
text indicates where the assessment is somewhat different to SREX Table 3-2. In each such case a footnote explains
why the assessment is different. See also Figures 2.32 and 2.33.
There are some exceptions to this large-scale warming of temperature extremes including central North
America, eastern USA (Alexander et al., 2006; Kunkel et al., 2008; Peterson et al., 2008) and some parts of
South America (Alexander et al., 2006; Rusticucci and Renom, 2008; Skansi et al., 2013) which indicate
changes consistent with cooling in these locations. However, these exceptions appear to be mostly associated
with changes in maximum temperatures (Donat et al., 2013c). The so-called ‘warming hole’ in central North
America and eastern USA, where temperatures have cooled relative to the significant warming elsewhere in
the region, is associated with observed changes in the hydrological cycle and land-atmosphere interaction
(Pan et al., 2004; Portmann et al., 2009a; Portmann et al., 2009b; Misra et al., 2012) and decadal and multidecadal variability linked with the Atlantic and Pacific Oceans (Meehl et al., 2012; Weaver, 2012).
[INSERT FIGURE 2.32 HERE]
Figure 2.32: Trends in annual frequency of extreme temperatures over the period 1951–2010, for: (a) cold nights
(TN10p), (b) cold days (TX10p), (c) warm nights (TN90p) and (d) warm days (TX90p) (Box 2.4, Table 1). Trends were
calculated only for grid boxes that had at least 40 years of data during this period and where data ended no earlier than
2003. Grey areas indicate incomplete or missing data. Black plus signs (+) indicate grid boxes where trends are
significant (i.e., a trend of zero lies outside the 90% confidence interval). The data source for trend maps is HadEX2
(Donat et al., 2013c) updated to include the latest version of the European Climate Assessment dataset (Klok and Tank,
2009). Beside each map are the near-global time series of annual anomalies of these indices with respect to 1961–1990
for three global indices datasets: HadEX2 (red); HadGHCND (Caesar et al., 2006; blue) and updated to 2010 and
GHCNDEX (Donat et al., 2013a; green). Global averages are only calculated using grid boxes where all three datasets
have at least 90% of data over the time period. Trends are significant (i.e., a trend of zero lies outside the 90%
confidence interval) for all the global indices shown.
Since AR4 many studies have analysed local to regional changes in multi-day temperature extremes in more
detail, specifically addressing different heat wave aspects such as frequency, intensity, duration and spatial
extent (Box 2.4, FAQ 2.2). Several high-profile heat waves have occurred in recent years (e.g., in Europe in
2003 (Beniston, 2004), Australia in 2009 (Pezza et al., 2012), Russia in 2010 (Barriopedro et al., 2011; Dole
et al., 2011; Trenberth and Fasullo, 2012a) and USA in 2011/2012 (Hoerling et al., 2012) (Section 10.6.2)
which have had severe impacts (see WGII). Heat waves are often associated with quasi-stationary
anticyclonic circulation anomalies that produce prolonged hot conditions at the surface (Black and Sutton,
2007; Garcia-Herrera et al., 2010), but long-term changes in the persistence of these anomalies are still
relatively poorly understood (Section 2.7). Heat waves can also be amplified by pre-existing dry soil
conditions in transitional climate zones (Ferranti and Viterbo, 2006; Fischer et al., 2007; Seneviratne et al.,
2010; Mueller and Seneviratne, 2012) and the persistence of those soil-mositure anomalies (Lorenz et al.,
2010). Dry soil-moisture conditions are either induced by precipitation deficits (Della-Marta et al., 2007b;
Vautard et al., 2007), or evapotranspiration excesses (Black and Sutton, 2007; Fischer et al., 2007), or a
combination of both (Seneviratne et al., 2010). This amplification of soil moisture-temperature feedbacks is
suggested to have partly enhanced the duration of extreme summer heat waves in southeastern Europe during
the latter part of the 20th century (Hirschi et al., 2011), with evidence emerging of a signature in other
moisture-limited regions (Mueller and Seneviratne, 2012).
Table 2.13 shows that there has been a likely increasing trend in the frequency of heatwaves since the middle
of the 20th century in Europe and Australia and across much of Asia where there are sufficient data.
However confidence on a global scale is medium due to lack of studies over Africa and South America but
also in part due to differences in trends depending on how heatwaves are defined (Perkins et al., 2012).
Using monthly means as a proxy for heatwaves Coumou et al. (2013) and Hansen et al. (2012) indicate that
record breaking temperatures in recent decades substantially exceed what would be expected by chance but
caution is required when making inferences between these studies and those that deal with multi-day events
and/or use more complex definitions for heatwave events. There is also evidence in some regions that
periods prior to the 1950s had more heatwaves (e.g., over the USA, the decade of the 1930s stands out and is
also associated with extreme drought conditions (Peterson et al., 2013) while conversely in other regions
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heatwave trends may have been underestimated due to poor quality and/or consistency of data (e.g., DellaMarta et al. (2007a) over Western Europe; Kuglitsch et al. (2009; 2010) over the Mediterranean). Recent
available studies also suggest that the number of cold spells has reduced significantly since the 1950s (Donat
et al., 2013a; Donat et al., 2013c).
In summary, new analyses continue to support the AR4 and SREX conclusions that since about 1950 it is
very likely that the numbers of cold days and nights have decreased and the numbers of warm days and
nights have increased overall on the global scale, i.e., for land areas with sufficient data. It is likely that such
changes have also occurred across most of North America, Europe, Asia and Australia. There is low to
medium confidence in historical trends in daily temperature extremes in Africa and South America as there is
either insufficient data or trends vary across these regions. This combined with issues with defining events,
leads to the assessment that there is medium confidence that globally the length and frequency of warm
spells, including heat waves, has increased since the middle of the 20th century although it is likely that
heatwave frequency has increased during this period in large parts of Europe, Asia and Australia.
2.6.2
Extremes of the Hydrological Cycle
In Section 2.5 mean state changes in different aspects of the hydrological cycle are discussed. In this section
we focus on the more extreme aspects of the cycle including extreme rainfall, severe local weather events
like hail, flooding and droughts. Extreme events associated with tropical and extratropical storms are
discussed in Sections 2.6.3 and 2.6.4 respectively.
2.6.2.1
Precipitation Extremes
AR4 concluded that substantial increases are found in heavy precipitation events. It was likely that annual
heavy precipitation events had disproportionately increased compared to mean changes between 1951 and
2003 over many mid-latitude regions, even where there had been a reduction in annual total precipitation.
Rare precipitation (such as the highest annual daily precipitation total) events were likely to have increased
over regions with sufficient data since the late 19th century. SREX supported this view, as have subsequent
analyses, but noted large spatial variability within and between regions (Table 3.2 of Seneviratne et al.,
2012).
Given the diverse climates across the globe it has been difficult to provide a universally valid definition of
‘extreme precipitation’. However, Box 2.4 Table 1 indicates some of the common definitions that are used in
the scientific literature. In general, statistical tests indicate changes in precipitation extremes are consistent
with a wetter climate (Section 7.6.5), although with a less spatially coherent pattern of change than
temperature, in that there are large areas that show increasing trends and large areas that show decreasing
trends and a lower level of statistical significance than for temperature change (Alexander et al., 2006; Donat
et al., 2013a; Donat et al., 2013c). Using R95p and SDII indices (Box 2.4), Figures 2.33a and 2.33b show
these areas for heavy precipitation amounts and precipitation intensity where sufficient data are available in
the HadEX2 dataset (Donat et al., 2013c) although there are more areas showing significant increases than
decreases. While changes in large-scale circulation patterns have a substantial influence on precipitation
extremes globally (Alexander et al., 2009; Kenyon and Hegerl, 2010), Westra et al. (2013) showed, using in
situ data over land, that trends in the wettest day of the year indicate more increases than would be expected
by chance. Over the tropical oceans satellite measurements show an increase in the frequency of the heaviest
rainfall during warmer (El Niño) years (Allan and Soden, 2008).
Regional trends in precipitation extremes since the middle of the 20th century are varied (Table 2.13). In
most continents confidence in trends is not higher than medium except in North America and Central
America and Europe where there have been likely increases in either the frequency or intensity of heavy
precipitation. This assessment increases to very likely for central North America. For North America it is also
likely that increases have occurred during the whole of the 20th century (Pryor et al., 2009; Donat et al.,
2013c; Villarini et al., 2013). For South America the most recent integrative studies indicate heavy rain
events are increasing in frequency and intensity over the contient as a whole (Donat et al., 2013c; Skansi et
al., 2013). For Europe and the Mediterranean, the assessment masks some regional and seasonal variation.
For example, much of the increase reported in Table 2.13 is found in winter although with decreasing trends
in some other regions such as northern Italy, Poland and some Mediterranean coastal sites (Pavan et al.,
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2008; Lupikasza, 2010; Toreti et al., 2010). There are mixed regional trends across Asia and Oceania but
with some indication that increases are being observed in more regions than decreases while recent studies
focused on Africa, in general, have not found significant trends in extreme precipitation (see Chapter 14 for
more on regional variations and trends).
The above studies generally use indices which reflect ‘moderate’ extremes e.g., events occurring as often as
5% or 10% of the time (Box 2.4). Only a few regions have sufficient data to assess trends in rarer
precipitation events reliably, e.g., events occurring on average once in several decades. Using Extreme Value
Theory, DeGaetano (2009) showed a 20% reduction in the return period for extreme precipitation events
over large parts of the contiguous USA from 1950 to 2007. For Europe from 1951 to 2010, Van den
Besselaar et al. (2012) reported a median reduction in 5 to 20 year return periods of 21%, with a range
between 2% and 58% depending on the subregion and season. This decrease in waiting times for rare
extremes is qualitatively similar to the increase in moderate extremes for these regions reported above, and
also consistent with earlier local results for the extreme tail of the distribution reported in AR4.
The above studies refer to daily precipitation extremes, although rainfall will often be limited to part of the
day only. The literature on sub-daily scales is too limited for a global assessment although it is clear that
analysis and framing of questions regarding sub-daily precipitation extremes is becoming more critical
(Trenberth, 2011). Available regional studies have shown results that are even more complex than for daily
precipitation and with variations in the spatial patterns of trends depending on event formulation and
duration. However regional studies show indications of more increasing than decreasing trends (Sen Roy,
2009; for India) (Sen Roy and Rouault, 2013; for South Africa) (Westra and Sisson, 2011; for Australia).
Some studies present evidence of scaling of sub-daily precipitation with temperature that is outside that
expected from the Clausius-Clapeyron relation (about 7% °C–1) (Lenderink and Van Meijgaard, 2008;
Haerter et al., 2010; Jones et al., 2010; Lenderink et al., 2011; Utsumi et al., 2011), but scaling beyond that
expected from thermodynamic theories is controversial (Section 7.6.5).
In summary, further analyses continue to support the AR4 and SREX conclusions that it is likely that since
1951 there have been statistically significant increases in the number of heavy precipitation events (e.g.,
above the 95th percentile) in more regions than there have been statistically significant decreases, but there
are strong regional and subregional variations in the trends. In particular, many regions present statistically
non-significant or negative trends, and, where seasonal changes have been assessed, there are also variations
between seasons (e.g., more consistent trends in winter than in summer in Europe). The overall most
consistent trends towards heavier precipitation events are found in central North America (very likely
increase) but assessment for Europe shows likely increases in more regions than decreases.
2.6.2.2
Floods
AR4 WGI Chapter 3 (Trenberth et al., 2007) did not assess changes in floods but AR4 WGII concluded that
there was not a general global trend in the incidence of floods (Kundzewicz et al., 2007). SREX went further
to suggest that there was low agreement and thus low confidence at the global scale regarding changes in the
magnitude or frequency of floods or even the sign of changes.
AR5 WGII assess floods in regional detail accounting for the fact that trends in floods are strongly
influenced by changes in river management (see also Section 2.5.2). While the most evident flood trends
appear to be in northern high latitudes, where observed warming trends have been largest, in some regions
no evidence of a trend in extreme flooding has been found, e.g., over Russia based on daily river discharge
(Shiklomanov et al., 2007). Other studies for Europe (Hannaford and Marsh, 2008; Renard et al., 2008;
Petrow and Merz, 2009; Stahl et al., 2010) and Asia (Jiang et al., 2008; Delgado et al., 2010) show evidence
for upward, downward or no trend in the magnitude and frequency of floods, so that there is currently no
clear and widespread evidence for observed changes in flooding except for the earlier spring flow in snowdominated regions (Seneviratne et al., 2012).
In summary, there continues to be a lack of evidence and thus low confidence regarding the sign of trend in
the magnitude and/or frequency of floods on a global scale.
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2.6.2.3
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Droughts
AR4 concluded that droughts had become more common, especially in the tropics and sub-tropics since
about 1970. SREX provided a comprehensive assessment of changes in observed droughts (Section 3.5.1 and
Box 3.3 of SREX), updated the conclusions provided by AR4 and stated that the type of drought considered
and the complexities in defining drought (Annex III: Glossary) can substantially affect the conclusions
regarding trends on a global scale (Chapter 10). Based on evidence since AR4, SREX concluded that there
were not enough direct observations of dryness to suggest high confidence in observed trends globally,
although there was medium confidence that since the 1950s some regions of the world have experienced
more intense and longer droughts. The differences between AR4 and SREX are primarily due to analyses
post-AR4, differences in how both assessments considered drought and updated IPCC uncertainty guidance.
There are very few direct measurements of drought related variables, such as soil moisture (Robock et al.,
2000), so drought proxies (e.g., PDSI, SPI, SPEI; Box 2.4) and hydrological drought proxies (e.g., Vidal et
al., 2010; Dai, 2011b) are often used to assess drought. The chosen proxy (e.g., precipitation,
evapotranspiration, soil moisture, or streamflow) and time scale can strongly affect the ranking of drought
events (Sheffield et al., 2009; Vidal et al., 2010). Analyses of these indirect indices come with substantial
uncertainties. For example, PDSI may not be comparable across climate zones. A self-calibrating (sc-) PDSI
can replace the fixed empirical constants in PDSI with values representative of the local climate (Wells et al.,
2004). Furthermore, for studies using simulated soil moisture, the type of potential evapotranspiration model
used can lead to significant differences in the estimation of the regions affected and the areal extent of
drought (Sheffield et al., 2012), but the overall effect of a more physically realistic parameterisation is
debated (van der Schrier et al., 2013).
Because drought is a complex variable and can at best be incompletely represented by commonly used
drought indices, discrepancies in the interpretation of changes can result. For example, Sheffield and Wood
(2008) found decreasing trends in the duration, intensity and severity of drought globally. Conversely, Dai
(2011a; Dai, 2011b) found a general global increase in drought, although with substantial regional variation
and individual events dominating trend signatures in some regions (e.g., the 1970s prolonged Sahel drought
and the 1930s drought in the USA and Canadian Prairies). Studies subsequent to these continue to provide
somewhat different conclusions on trends in global droughts and/or dryness since the middle of the 20th
century (Sheffield et al., 2012; Dai, 2013; Donat et al., 2013c; van der Schrier et al., 2013).
Van der Schrier et al. (2013) using monthly sc-PDSI found no strong case either for notable drying or
moisture increase on a global scale over the periods 1901–-2009 or 1950–2009 and this largely agrees with
the results of Sheffield et al. (2012) over the latter period. A comparison between the sc-PDSI calculated by
van der Schrier et al. (2013) and that of Dai (2011a) shows that the dominant mode of variability is very
similar, with a temporal evolution suggesting a trend toward drying. However, the same analysis for the
1950–2009 period shows an initial increase in drying in the Van der Schrier et al. dataset, followed by a
decrease from the mid-1980s onwards, while the Dai data show a continuing increase until 2000. The
difference in trends between the sc-PDSI dataset of Van der Schrier et al. and Dai appears to be due to the
different calibration periods used, the shorter 1950–1979 period in the latter study resulting in higher index
values from 1980 onwards, although the associated spatial patterns are similar. In addition, the observed
precipitation forcing dataset differs between studies, with van der Schrier et al. (2013) and Sheffield et al.
(2012) using CRU TS 3.10.01 (updated from Mitchell and Jones, 2005). This dataset uses fewer stations and
has been wetter than some other precipitation products in the last couple of decades (Figure 2.29, Table 2.9),
although the best dataset to use is still an open question. Despite this, a measure of sc-PDSI with potential
evapotranspiration estimated using the Penman-Montieth equation shows an increase in the percentage of
land area in drought since 1950 (Sheffield et al., 2012; Dai, 2013), while van der Schrier et al. (2013) also
finds a slight increase in the percentage of land area in severe drought using the same measure. This is
qualitatively consistent with the trends in surface soil moisture found for the shorter period 1988–2010 by
Dorigo et al. (2012) using a new multi-satellite dataset and changes in observed streamflow (Dai, 2011b).
However all these studies draw somewhat different conclusions and the compelling arguments both for (Dai,
2011b; Dai, 2013) and against (Sheffield et al., 2012; van der Schrier et al., 2013) a significant increase in
the land area experiencing drought has hampered global assessment.
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Studies that support an increasing trend towards the land area affected by drought seem to be at odds with
studies that look at trends in dryness (i.e., lack of rainfall). For example, Donat et al. (2013c) found that the
annual maximum number of consecutive dry days has declined since the 1950s in more regions than it has
increased (Figure 2.33c). However, only regions in Russia and the USA indicate significant changes and
there is a lack of information for this index over large regions, especially Africa. Most other studies
focussing on global dryness find similar results, with decadal variability dominating longer-term trends
(Frich et al., 2002; Alexander et al., 2006; Donat et al., 2013a). However, Giorgi et al. (2011) indicate that
‘hydroclimatic intensity’ (Box 2.4, Chapter 7), a measure which combines both dry spell length and
precipitation intensity, has increased over the latter part of the 20th century in response to a warming
climate. They show that positive trends (reflecting an increase in the length of drought and/or extreme
precipitation events) are most marked in Europe, India, parts of South America and East Asia although
trends appear to have decreased (reflecting a decrease in the length of drought and/or extreme precipitation
events) in Australia and northern South America (Figure 2.33c). Data availability, quality and length of
record remain issues in drawing conclusions on a global scale, however.
Despite differences between the conclusions drawn by global studies, there are some areas in which they
agree. Table 2.13 indicates that there is medium confidence of an increase in dryness or drought in Eastern
Asia with high confidence that this is the case in the Mediterannean and west Africa. There is also high
confidence of decreases in dryness or drought in central North America and north-west Australia.
[INSERT FIGURE 2.33 HERE]
Figure 2.33: Trends in (a) annual amount of precipitation from days >95th percentile (R95p) (b) daily precipitation
intensity (SDII) and (c) frequency of the annual maximum number of consecutive dry days (CDD) (Box 2.4, Table 1).
Trends are shown as relative values for better comparison across different climatic regions. Trends were calculated only
for grid boxes that had at least 40 years of data during this period and where data ended no earlier than 2003. Grey areas
indicate incomplete or missing data. Black plus signs (+) indicate grid boxes where trends are significant (i.e., a trend of
zero lies outside the 90% confidence interval). The data source for trend maps is HadEX2 (Donat et al., 2013a) updated
to include the latest version of the European Climate Assessment dataset (Klok and Tank, 2009). (d) Trends
(normalized units) in hydroclimatic intensity (HY-INT: a multiplicative measure of length of dry spell and precipitation
intensity) over the period 1976–2000 (adapted from Giorgi et al. (2011). An increase (decrease) in HY-INT reflects an
increase (decrease) in the length of drought and /or extreme precipitation events.
In summary, the current assessment concludes that there is not enough evidence at present to suggest more
than low confidence in a global-scale observed trend in drought or dryness (lack of rainfall) since the middle
of the 20th century, due to lack of direct observations, geographical inconsistencies in the trends, and
dependencies of inferred trends on the index choice. Based on updated studies, AR4 conclusions regarding
global increasing trends in drought since the 1970s were probably overstated. However, it is likely that the
frequency and intensity of drought has increased in the Mediterranean and West Africa and decreased in
central North America and north-west Australia since 1950.
2.6.2.4
Severe Local Weather Events
Another extreme aspect of the hydrological cycle is severe local weather phenomena such as hail or thunder
storms. These are not well observed in many parts of the world, since the density of surface meteorological
observing stations is too coarse to measure all such events. Moreover, homogeneity of existing reporting is
questionable (Verbout et al., 2006; Doswell et al., 2009). Alternatively, measures of severe thunderstorms or
hailstorms can be derived by assessing the environmental conditions that are favourable for their formation
but this method is very uncertain (Seneviratne et al., 2012). SREX highlighted studies such as Brooks and
Dotzek (2008) who found significant variability but no clear trend in the past 50 years in severe
thunderstorms in a region east of the Rocky Mountains in the United States, Cao (2008) who found an
increasing frequency of severe hail events in Ontario, Canada during the period 1979–2002 and Kunz et al.
(2009) who found that hail days significantly increased during the period 1974–2003 in southwest Germany.
Hailpad studies from Italy (Eccel et al., 2012) and France (Berthet et al., 2011) suggest slight increases in
larger hail sizes and a correlation between the fraction of precipitation falling as hail with average summer
temperature while in Argentina between 1960–2008 the annual number of hail events was found to be
increasing in some regions and decreasing in others (Mezher et al., 2012). In China between 1961 and 2005,
the number of hail days has been found to generally decrease, with the highest occurrence between 1960 and
1980 but with a sharp drop since the mid-1980s (CMA, 2007; Xie et al., 2008). However there is little
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consistency in hail size changes in different regions of China since 1980 (Xie et al., 2010). Remote sensing
offers a potential alterative to surface-based meteorological networks for detecting changes in small scale
severe weather phenomenon such as proxy measurements of lightning from satellites (Zipser et al., 2006) but
there remains little convincing evidence that changes in severe thunderstorms or hail have occurred since the
middle of the 20th century (Brooks, 2012).
In summary, there is low confidence in observed trends in small-scale severe weather phenomena such as
hail and thunderstorms because of historical data inhomogeneities and inadequacies in monitoring systems.
2.6.3
Tropical Storms
AR4 concluded that it was likely that an increasing trend had occurred in intense tropical cyclone activity
since 1970 in some regions but that there was no clear trend in the annual numbers of tropical cyclones.
Subsequent assessments, including SREX and more recent literature indicate that it is difficult to draw firm
conclusions with respect to the confidence levels associated with observed trends prior to the satellite era and
in ocean basins outside of the North Atlantic.
Section 14.6.1 discusses changes in tropical storms in detail. Current datasets indicate no significant
observed trends in global tropical cyclone frequency over the past century and it remains uncertain whether
any reported long-term increases in tropical cyclone frequency are robust, after accounting for past changes
in observing capabilities (Knutson et al., 2010). Regional trends in tropical cyclone frequency and the
frequency of very intense tropical cyclones have been identified in the North Atlantic and these appear
robust since the 1970s (Kossin et al. 2007) (very high confidence). However, argument reigns over the cause
of the increase and on longer time scales the fidelity of these trends is debated (Landsea et al., 2006; Holland
and Webster, 2007; Landsea, 2007; Mann et al., 2007b) with different methods for estimating undercounts in
the earlier part of the record providing mixed conclusions (Chang and Guo, 2007; Mann et al., 2007a;
Kunkel et al., 2008; Vecchi and Knutson, 2008, 2011). No robust trends in annual numbers of tropical
storms, hurricanes and major hurricanes counts have been identified over the past 100 years in the North
Atlantic basin. Measures of land-falling tropical cyclone frequency (Figure 2.34) are generally considered to
be more reliable than counts of all storms which tend to be strongly influenced by those that are weak and/or
short-lived. Callaghan and Power (2011) find a statistically significant decrease in Eastern Australia landfalling tropical cyclones since the late 19th century although including 2010/2011 season data this trend
becomes non-significant (i.e., a trend of zero lies just inside the 90% confidence interval). Significant trends
are not found in other oceans on shorter timescales (Chan and Xu, 2009; Kubota and Chan, 2009; Mohapatra
et al., 2011; Weinkle et al., 2012), although Grinsted et al. (2012) find a significant positive trend in eastern
USA using tide-guage data from 1923–2008 as a proxy for storm surges associated with land-falling
hurricanes. Differences between tropical cyclone studies highlight the challenges that still lie ahead in
assessing long-term trends.
[INSERT FIGURE 2.34 HERE]
Figure 2.34: Normalized 5-year running means of the number of (a) adjusted land falling eastern Australian tropical
cyclones (adapted from Callaghan and Power (2011) and updated to include 2010/2011 season) and (b) unadjusted land
falling U.S. hurricanes (adapted from Vecchi and Knutson (2011) and (c) land-falling typhoons in China (adapted from
CMA, 2011). Vertical axis ticks represent one standard deviation, with all series normalized to unit standard deviation
after a 5-year running mean was applied.
Arguably, storm frequency is of limited usefulness if not considered in tandem with intensity and duration
measures. Intensity measures in historical records are especially sensitive to changing technology and
improving methodology. However over the satellite era, increases in the intensity of the strongest storms in
the Atlantic appear robust (Kossin et al., 2007; Elsner et al., 2008) but there is limited evidence for other
regions and the globe. Time series of cyclone indices such as power dissipation, an aggregate compound of
tropical cyclone frequency, duration, and intensity that measures total wind energy by tropical cyclones,
show upward trends in the North Atlantic and weaker upward trends in the western North Pacific since the
late 1970s (Emanuel, 2007), but interpretation of longer-term trends is again constrained by data quality
concerns (Landsea et al., 2011).
In summary, this assessment does not revise the SREX conclusion of low confidence that any reported longterm (centennial) increases in tropical cyclone activity are robust, after accounting for past changes in
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observing capabilities. More recent assessments indicate that it is unlikely that annual numbers of tropical
storms, hurricanes and major hurricanes counts have increased over the past 100 years in the North Atlantic
basin. Evidence however is for a virtually certain increase in the frequency and intensity of the strongest
tropical cyclones since the 1970s in that region.
2.6.4
Extratropical Storms
AR4 noted a likely net increase in frequency/intensity of NH extreme extratropical cyclones and a poleward
shift in storm tracks since the 1950s. SREX further consolidated the AR4 assessment of poleward shifting
storm tracks, but revised the assessment of the confidence levels associated with regional trends in the
intensity of extreme extratropical cyclones.
Studies using reanalyses continue to support a northward and eastward shift in the Atlantic cyclone activity
during the last 60 years with both more frequent and more intense wintertime cyclones in the high-latitude
Atlantic (Schneidereit et al., 2007; Raible et al., 2008; Vilibic and Sepic, 2010) and fewer in the mid-latitude
Atlantic (Wang et al., 2006b; Raible et al., 2008). Some studies show an increase in intensity and number of
extreme Atlantic cyclones (Paciorek et al., 2002; Lehmann et al., 2011) while others show opposite trends in
eastern Pacific and North America (Gulev et al., 2001). Comparisons between studies are hampered because
of the sensitivities in identification schemes and/or different definitions for extreme cyclones (Ulbrich et al.,
2009; Neu et al., 2012). The fidelity of research findings also rests largely with the underlying reanalyses
products that are used (Box 2.3). See also Section 14.6.2.
Over longer periods studies of severe storms or storminess have been performed for Europe where long
running in situ pressure and wind observations exist. Direct wind speed measurements however either have
short records or are hampered by inconsistencies due to changing instrumentation and observing practice
over time (Smits et al., 2005; Wan et al., 2010). In most cases, therefore wind speed or storminess proxies
are derived from in situ pressure measurements or reanalyses data, the quality and consistency of which vary.
In situ observations indicate no clear trends over the past century or longer (Hanna et al., 2008; Matulla et
al., 2008; Allan et al., 2009; Barring and Fortuniak, 2009) with substantial decadal and longer fluctuations
but with some regional and seasonal trends (Wang et al., 2009c; Wang et al., 2011). Figure 2.35 shows some
of these changes for boreal winter using geostrophic wind speeds indicating that decreasing trends
outnumber increasing trends (Wang et al., 2011) although with few that are statistically significant. While
Donat et al. (2011) and Wang et al. (2012h) find significant increases in both the strength and frequency of
wintertime storms for large parts of Europe using the 20th century Reanalysis (Compo et al., 2011), there is
debate over whether this is an artefact of the changing number of assimilated observations over time (Cornes
and Jones, 2011; Krueger et al., 2013) even though Wang et al. (2012h) find good agreement between the
20th century Reanalysis trends and those derived from geostropic wind extremes in the North Sea region.
[INSERT FIGURE 2.35 HERE]
Figure 2.35: 99th percentiles of geostrophic wind speeds for winter (DJF). Triangles show regions where geostrophic
wind speeds have been calculated from in situ surface pressure observations. Within each pressure triangle, Gaussian
low-pass filtered curves and estimated linear trends of the 99th percentile of these geostrophic wind speeds for winter
are shown. The ticks of the time (horizontal) axis range from 1875 to 2005, with an interval of 10 years. Disconnections
in lines show periods of missing data. Red (blue) trend lines indicate upward (downward) significant trends (i.e., a trend
of zero lies outside the 95% confidence interval). From Wang et al. (2011).
SREX noted that available studies using reanalyses indicate a decrease in extratropical cyclone activity
(Zhang et al., 2004) and intensity (Zhang et al., 2004; Wang et al., 2009d) over the last 50 years has been
reported for northern Eurasia (60°N–40°N) linked to a possible northward shift with increased cyclone
frequency in the higher latitudes and decrease in the lower latitudes. The decrease at lower latitudes was also
found in East Asia (Wang et al., 2012h) and is also supported by a study of severe storms by Zou et al.
(2006b) who used sub-daily in situ pressure data from a number of stations across China.
SREX also notes that, based on reanalyses, North American cyclone numbers have increased over the last 50
years, with no statistically significant change in cyclone intensity (Zhang et al., 2004). Hourly SLP data from
Canadian stations showed that winter cyclones have become significantly more frequent, longer lasting, and
stronger in the lower Canadian Arctic over the last 50 years (1953–2002), but less frequent and weaker in the
south, especially along the southeast and southwest Canadian coasts (Wang et al., 2006a). Further south, a
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tendency toward weaker low-pressure systems over the past few decades was found for U.S. east coast
winter cyclones using reanalyses, but no statistically significant trends in the frequency of occurrence of
systems (Hirsch et al., 2001).
Using the 20th century Reanalysis (Compo et al., 2011), Wang et al. (2012h) found substantial increases in
extratropical cyclone activity in the SH (20°S–90°S). However, for southeast Australia, a decrease in activity
is found and this agrees well with geostrophic wind extremes derived from in situ surface pressure
observations (Alexander et al., 2011). This strengthens the evidence of a southward shift in storm tracks
previously noted using older reanalyses products (Fyfe, 2003; Hope et al., 2006). Frederiksen and
Frederiksen (2007) linked the reduction in cyclogenesis at 30°S and southward shift to a decrease in the
vertical mean meridional temperature gradient. There is some inconsistency among reanalysis products for
the SH regarding trends in the frequency of intense extratropical cyclones (Lim and Simmonds, 2007; Pezza
et al., 2007; Lim and Simmonds, 2009) although studies tend to agree on a trend towards more intense
systems, even when inhomogeneities associated with changing numbers of observations have been taken into
account (Wang et al., 2012h). However further undetected contamination of these trends due to issues with
the reanalyses products cannot be ruled out (Box 2.3) and this lowers our confidence in long-term trends.
Links between extratropical cyclone activity and large scale variability are discussed in Section 2.7 and
Section 14.6.2.
Studies that have examined trends in wind extremes from observations or regional reanalysis products tend
to point to declining trends in extremes in mid-latitudes (Pirazzoli and Tomasin, 2003; Smits et al., 2005;
Pryor et al., 2007; Zhang et al., 2007b) and increasing trends in high latitudes (Lynch et al., 2004; Turner et
al., 2005; Hundecha et al., 2008; Stegall and Zhang, 2012). Other studies have compared the trends from
observations with reanalysis data and reported differing or even opposite trends in the reanalysis products
(Smits et al., 2005; McVicar et al., 2008). On the other hand, declining trends reported by Xu et al. (2006b)
over China between 1969 and 2000 were generally consistent with trends in NCEP reanalysis. Trends
extracted from reanalysis products must be treated with caution however, although usually with later
generation products providing improvements over older products (Box 2.3).
In summary, confidence in large scale changes in the intensity of extreme extratropical cyclones since 1900
is low. There is also low confidence for a clear trend in storminess proxies over the last century due to
inconsistencies between studies or lack of long-term data in some parts of the world (particularly in the SH).
Likewise, confidence in trends in extreme winds is low, due to quality and consistency issues with analysed
data.
[START BOX 2.4 HERE]
Box 2.4: Extremes Indices
As SREX highlighted, there is no unique definition of what constitutes a climate extreme in the scientific
literature given variations in regions and sectors affected (Stephenson et al., 2008). Much of the available
research is based on the use of so-called ‘extremes indices’ (Zhang et al., 2011). These indices can either be
based on the probability of occurrence of given quantities or on absolute or percentage threshold
exceedances (relative to a fixed climatological period) but also include more complex definitions related to
duration, intensity and persistence of extreme events. For example, the term ‘heat wave’ can mean very
different things depending on the index formulation for the application for which it is required (Perkins and
Alexander, 2012).
Box 2.4, Table 1 lists a number of specific indices that appear widely in the literature and have been chosen
to provide some consistency across multiple chapters in AR5 (along with the location of associated figures
and text). These indices have been generally chosen for their robust statistical properties and their
applicability across a wide range of climates. Another important criterion is that data for these indices are
broadly available over both space and time. The existing near-global land-based datasets cover at least the
post-1950 period but for regions such as Europe, North America, parts of Asia and Australia much longer
analyses are available. The same indices used in observational studies (this chapter) are also used to diagnose
climate model output (Chapters 9, 10, 11 and 12).
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Box 2.4, Table 1: Definitions of extreme temperature and precipitation indices used in IPCC. After Zhang et al. (2011).
The most common units are shown but these may be shown as normalized or relative depending on application in
different chapters.
Index
Descriptive name
Definition
TXx
Warmest daily Tmax
TNx
Warmest daily Tmin
TXn
Coldest daily Tmax
TNn
Coldest daily Tmin
TN10p
Cold nights
TX10p
Cold days
TN90p
Warm nights
TX90p
Warm days
FD
Frost days
TR
Tropical nights
Units
Figures/Tables
Section
Seasonal/annual maximum value of ºC
daily max temperature
Box 2.4, Figure 1,
Figures 9.37, 10.17,
12.13
Seasonal/annual maximum value of ºC
daily minimum temperature
Seasonal/annual minimum value of ºC
daily maximum temperature
Figures 9.37, 10.17
Box 2.4,
9.5.4.1,
10.6.1.1,
12.4.3.3
9.5.4.1,
10.6.1.1
9.5.4.1,
10.6.1.1,
12.4.3.3
9.5.4.1,
10.6.1.1
2.6.1, 9.5.4.1,
10.6.1.1,
11.3.2.5.1,
2.6.1, 9.5.4.1,
10.6.1.1,
11.3.2.5.1,
2.6.1, 9.5.4.1,
10.6.1.1,
11.3.2.5.1,
2.6.1, 9.5.4.1,
10.6.1.1,
11.3.2.5.1,
2.6.1, 9.5.4.1,
10.6.1.1,
12.4.3.3
9.5.4.1,
12.4.3.3
2.6.2.1,
9.5.4.1,
10.6.1.2,
12.4.5.5
9.5.4.1,
10.6.1.2,
12.4.5.5,
14.2.1
2.6.2.1,
9.5.4.1, 14.2.1
2.6.2.1,
9.5.4.1,
11.3.2.5.1,
2.6.2.3,
9.5.4.1,
12.4.5.5,
14.2.1
Figures 9.37, 10.17,
12.13
Seasonal/annual minimum value of
daily minimum temperature
Days (or fraction of time) when
daily minimum temperature <10th
percentile
Days (or fraction of time) when
daily maximum temperature <10th
percentile
Days (or fraction of time) when
daily minimum temperature >90th
percentile
Days (or fraction of time) when
daily maximum temperature >90th
percentile
Frequency of daily minimum
temperature <0°C
ºC
days
Figures 9.37, 12.13
RX1day Wettest day
Frequency of daily minimum
temperature >20°C
Maximum 1-day precipitation
mm
Figures 9.37, 10.10
Table 2.12, 12.27
RX5day Wettest consecutive
five days
Maximum of consecutive 5-day
precipitation
mm
Figures 9.37, 12.26,
14.1
SDII
Simple daily intensity
index
Precipitation from very
wet days
Ratio of annual total precipitation to mm day–1
the number of wet days (≥1 mm)
Amount of precipitation from days mm
>95th (99th) percentile
Consecutive dry days
Maximum number of consecutive
days when precipitation <1 mm
R95p
CDD
Figures 9.37, 10.17,
12.13
days (%) Figures 2.32, 9.37,
10.17
Tables 2.11, 2.12
days (%) Figures 2.32, 9.37,
10.17, 11.19
days (%) Figures 2.32, 9.37,
10.17
Tables 2.11, 2.12
days (%) Figures 2.32, 9.37,
10.17, 11.19, 11.20
Tables 2.11, 2.12
days
Figures 9.37, 12.13
Table 2.12
days
Figures 2.33, 9.37,
11.19, 14.1
Figures 2.33, 9.37,
11.19, 11.20
Table 2.12
Figures 2.33, 9.37,
12.26, 14.1
The types of indices discussed here do not include indices such as NINO3 representing positive and negative
phases of the ENSO (Box 2.5), nor do they include extremes such as 1 in 100 year events. Typically extreme
indices assessed here reflect more ‘moderate’ extremes, e.g., events occurring as often as 5% or 10% of the
time (Box 2.4, Table 1). Predefined extreme indices are usually easier to obtain than the underlying daily
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climate data, which are not always freely exchanged by meteorological services. However some of these
indices do represent rarer events e.g., annual maxima or minima. Analyses of these and rarer extremes (e.g.,
with longer return period thresholds) are making their way into a growing body of literature which, for
example, are using Extreme Value Theory (Coles, 2001) to study climate extremes (Zwiers and Kharin,
1998; Brown et al., 2008; Sillmann et al., 2011; Zhang et al., 2011; Kharin et al., 2013).
Extreme indices are more generally defined for daily temperature and precipitation characteristics (Zhang et
al., 2011) although research is developing on the analysis of sub-daily events but mostly only on regional
scales (Sen Roy, 2009; Shiu et al., 2009; Jones et al., 2010; Jakob et al., 2011; Lenderink et al., 2011; Shaw
et al., 2011). Temperature and precipitation indices are sometimes comined to investigate ‘extremeness’
(e.g., hydroclimatic intensity, HY-INT; Giorgi et al., 2011) and/or the areal extent of extremes (e.g., the
Climate Extremes Index (CEI) and its variants (Gleason et al., 2008; Gallant and Karoly, 2010; Ren et al.,
2011). Indices rarely include other weather and climate variables, such as wind speed, humidity, or physical
impacts (e.g., streamflow) and phenomena. Some examples are available in the literature for wind-based
(Della-Marta et al., 2009) and pressure-based (Beniston, 2009) indices, for health-relevant indices combining
temperature and relative humidity characteristics (Diffenbaugh et al., 2007; Fischer and Schar, 2010) and for
a range of dryness or drought indices (e.g., Palmer Drought Severity Index (PDSI) Palmer, 1965;
Standardised Precipitation Index (SPI), Standardised Precipitation Evapotranspiration Index (SPEI) VicenteSerrano et al., 2010) and wetness indices (e.g., Standardized Soil Wetness Index (SSWI); Vidal et al., 2010).
In addition to the complication of defining an index, the results depend also on the way in which indices are
calculated (to create global averages for example). This is due to the fact that different algorithms may be
employed to create grid box averages from station data, or that extremes indices may be calculated from
gridded daily data or at station locations and then gridded. All of these factors add uncertainty to the
calculation of an extreme. For example, the spatial patterns of trends in the hottest day of the year differ
slightly between datasets although when globally averaged, trends are similar over the second half of the
20th century (Box 2.4, Figure 1). Further discussion of the parametric and structural uncertainties in datasets
is given in Box 2.1.
[INSERT BOX 2.4, FIGURE 1 HERE]
Box 2.4, Figure 1: Trends in the warmest day of the year using different datasets for the period 1951–2010. The
datasets are (a) HadEX2 (Donat et al., 2013c) updated to include the latest version of the European Climate Assessment
dataset (Klok and Tank, 2009), (b) HadGHCND (Caesar et al., 2006) using data updated to 2010 (Donat et al., 2013a),
and (c) Globally averaged annual warmest day anomalies for each dataset. Trends were calculated only for grid boxes
that had at least 40 years of data during this period and where data ended no earlier than 2003. Grey areas indicate
incomplete or missing data. Black plus signs (+) indicate grid boxes where trends are significant (i.e., a trend of zero
lies outside the 90% confidence interval). Anomalies are only calculated using grid boxes where both datasets have data
and where 90% of data are available.
[END BOX 2.4 HERE]
[START FAQ 2.2 HERE]
FAQ 2.2: Have there been any Changes in Climate Extremes?
There is strong evidence that warming has lead to changes in temperature extremes—including heat waves—
since the mid-20th century. Increases in heavy precipitation have probably also occurred over this time, but
vary by region. However, for other extremes, such as tropical cyclone frequency, we are less certain, except
in some limited regions, that there have been discernable changes over the observed record.
From heat waves to cold snaps or droughts to flooding rains, recording and analysing climate extremes poses
unique challenges, not just because these events are rare, but also because they invariably happen in
conjunction with disruptive conditions. Furthermore, there is no consistent definition in the scientific
literature of what constitutes an extreme climatic event, and this complicates comparative global
assessments.
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Although, in an absolute sense, an extreme climate event will vary from place to place—a hot day in the
tropics, for instance, may be a different temperature to a hot day in the mid-latitudes—international efforts to
monitor extremes have highlighted some significant global changes.
For example, using consistent definitions for cold (<10th percentile) and warm (>90th percentile) days and
nights it is found that warm days and nights have increased and cold days and nights have decreased for most
regions of the globe; a few exceptions being central and eastern North America, and southern South America
but mostly only related to daytime temperatures. Those changes are generally most apparent in minimum
temperature extremes e.g., warm nights. Data limitations make it difficult to establish a causal link to
increases in average temperatures, but FAQ 2.2, Figure 1 indicates that daily global temperature extremes
have indeed changed. Whether these changes are simply associated with the average of daily temperatures
increasing (the dashed lines in FAQ 2.2, Figure 1) or whether other changes in the distribution of daytime
and night-time temperatures have occurred is still under debate.
Warm spells or heat waves i.e., periods containing consecutive extremely hot days or nights have also been
assessed, but there are fewer studies of heat wave characteristics than those that compare changes in merely
warm days or nights. Most global land areas with available data have experienced more heat waves since the
middle of the 20th century. One exception is the south-eastern United States, where heat wave frequency and
duration measures generally show decreases. This has been associated with a so-called ‘warming hole’ in
this region, where precipitation has also increased, and may be related to interactions between the land and
the atmosphere, and long-term variations in the Atlantic and Pacific Oceans. However large regions,
particularly in Africa and South America have limited information on changes in heatwaves.
For regions such as Europe, where historical temperature reconstructions exist going back several hundreds
of years, indications are that some regions have experienced a disproportionate number of extreme heat
waves in recent decades.
[INSERT FAQ 2.2, FIGURE 1 HERE]
FAQ 2.2, Figure 1: Distribution of (a) daily minimum and (b) daily maximum temperature anomalies relative to a
1961–1990 climatology for two periods: 1951–1980 (blue) and 1981–2010 (red) using the HadGHCND dataset. The
shaded blue and red areas represent the coldest 10% and warmest 10% respectively of (a) nights and (b) days during the
1951–1980 period. The darker shading indicates by how much the number of the coldest days and nights has reduced
(dark blue) and by how much the number of the warmest days and nights has increased (dark red) during the 1981–2010
period compared to the 1951–1980 period.
Changes in extremes for other climate variables are generally less coherent than those observed for
temperature, due to data limitations and inconsistencies between studies, regions and/or seasons. However,
increases in precipitation extremes, for example, are consistent with a warmer climate. Analyses of land
areas with sufficient data indicate increases in the frequency and intensity of extreme precipitation events in
recent decades, but results vary strongly between regions and seasons. For instance, evidence is most
compelling for increases in heavy precipitation in North America, Central America and Europe, but in some
other regions—such as southern Australia and western Asia—there is evidence of decreases. Likewise,
drought studies do not agree on the sign of the global trend, with regional inconsistencies in trends also
dependent on how droughts are defined. However, indications exist that droughts have increased in some
regions (e.g., the Mediterranean) and decreased in others (e.g., central North America) since the middle of
the 20th century.
Considering other extremes, such as tropical cyclones, the latest assessments show that due to problems with
past observing capabilities, it is difficult to make conclusive statements about long-term trends. There is very
strong evidence, however, that storm activity has increased in the North Atlantic since the 1970s.
Over periods of a century or more, evidence suggests slight decreases in the frequency of tropical cyclones
making landfall in the North Atlantic and the South Pacific, once uncertainties in observing methods have
been considered. Little evidence exists of any longer-term trend in other ocean basins. For extratropical
cyclones, a poleward shift is evident in both hemispheres over the past 50 years, with further but limited
evidence of a decrease in wind storm frequency at mid-latitudes. Several studies suggest an increase in
intensity, but data sampling issues hamper these assessments.
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[INSERT FAQ 2.2, FIGURE 2 HERE]
FAQ 2.2, Figure 2: Trends in the frequency (or intensity) of various climate extremes (arrow direction denotes the sign
of the change) since the middle of the 20th century (except for North Atlantic storms where the period covered is from
the 1970s).
FAQ 2.2, Figure 2 summarizes some of the observed changes in climate extremes. Overall, the most robust
global changes in climate extremes are seen in measures of daily temperature, including to some extent, heat
waves. Precipitation extremes also appear to be increasing, but there is large spatial variability, and observed
trends in droughts are still uncertain except in a few regions. While robust increases have been seen in
tropical cyclone frequency and activity in the North Atlantic since the 1970s, the reasons for this are still
being debated. There is limited evidence of changes in extremes associated with other climate variables since
the mid-20th century.
[END FAQ 2.2 HERE]
2.7
Changes in Atmospheric Circulation and Patterns of Variability
Changes in atmospheric circulation and indices of climate variability, as expressed in Sea Level Pressure
(SLP), wind, Geopotential Height (GPH), and other variables were assessed in AR4. Substantial multidecadal variability was reported in the large-scale atmospheric circulation over the Atlantic and the Pacific.
With respect to trends, a decrease was found in tropospheric GPH over high latitudes of both hemispheres
and an increase over the mid-latitudes in boreal winter for the period 1979–2001. These changes were found
to be associated with an intensification and poleward displacement of Atlantic and southern midlatitude jet
streams and enhanced storm track activity in the NH from the 1960s to at least the 1990s. Changes in the
North Atlantic Oscillation (NAO) and the Southern Annular Mode (SAM) towards their positive phases were
observed, but it was noted that the NAO returned to its long-term mean state from the mid-1990s to the early
2000s.
Since AR4, more and improved observational datasets and reanalysis datasets (Box 2.3) have been
published. Uncertainties and inaccuracies in all datasets are better understood (Box 2.1). The studies since
AR4 assessed in this section support the poleward movement of circulation features since the 1970s and the
change in the SAM. At the same time, large decadal-to-multidecadal variability in atmospheric circulation is
found that partially offsets previous trends in other circulation features such as the NAO or the Pacific
Walker circulation.
This section assesses observational evidence for changes in atmospheric circulation in fields of SLP, GPH,
and wind, in circulation features (such as the Hadley and Walker circulation, monsoons, or jet streams;
Annex III: Glossary), as well as in circulation variability modes. Regional climate effects of the circulation
changes are discussed in Chapter 14.
2.7.1
Sea Level Pressure
AR4 concluded that SLP in December to February decreased between 1948 and 2005 in the Arctic, Antarctic
and North Pacific. More recent studies using updated data for the period 1949–2009 (Gillett and Stott, 2009)
also find decreases in SLP in the high latitudes of both hemispheres in all seasons and increasing SLP in the
tropics and subtropics most of the year. However, due to decadal variability SLP trends are sensitive to the
choice of the time period (Box 2.2), and they depend on the dataset.
The spatial distribution of SLP represents the distribution of atmospheric mass, which is the surface imprint
of the atmospheric circulation. Barometric measurements are made in weather stations or on board ships.
Fields are produced from the observations by interpolation or using data assimilation into weather models.
One of the most widely used observational datasets is HadSLP2 (Allan and Ansell, 2006), which integrates
2228 historical global terrestrial stations with marine observations from the ICOADS on a 5° × 5°grid. Other
observation products (e.g., Trenberth and Paolino, 1980; for the extratropical NH) or reanalyses are also
widely used to address changes in SLP. Although the quality of SLP data is considered good, there are
discrepancies between gridded SLP datasets in regions with sparse observations, e.g., over Antarctica (Jones
and Lister, 2007).
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Van Haren et al. (2012) found a strong SLP decrease over the Mediterranean in January to March from 1961
to 2000. For the more recent period (1979-2012) trends in SLP, consistent across different datasets (shown in
Figure 2.36 for ERA-Interim), are negative in the tropical and northern subtropical Atlantic during most of
the year as well as, in May to October, in northern Siberia. Positive trends are found year-round over the
North and South Pacific and South Atlantic. Trends in the equatorial Pacific zonal SLP gradient during the
20th century (e.g., Vecchi et al., 2006; Power and Kociuba, 2011a, 2011b) are discussed in Section 2.7.5.
[INSERT FIGURE 2.36 HERE]
Figure 2.36: Trends in (left) Sea Level Pressure (SLP), (middle) 500 hPa geopotential height (GPH), and (right) 100
hPa GPH in (top) November to April 1979/1980 to 2011/2012 and (bottom) May to October 1979 to 2011 from ERAInterim data. Trends are only shown if significant (i.e., a trend of zero lies outside the 90% confidence interval).
The position and strength of semi-permanent pressure centres show no clear evidence for trends since 1951.
However, prominent variability is found on decadal time scales (Figure 2.37). Consistent across different
datasets, the Azores high and the Icelandic low in boreal winter, as captured by the high and low SLP
contours, were both small in the 1960s and 1970s, large in the 1980s and 1990s, and again smaller in the
2000s. Favre and Gershunov (2006) find an eastward shift of the Aleutian low from the mid-1970s to 2001,
which persisted during the 2000s (Figure 2.37). The Siberian High exhibits pronounced decadal-tomultidecadal variability (Panagiotopoulos et al., 2005; Huang et al., 2010), with a recent (1998 to 2012)
strengthening and northwestward expansion (Zhang et al., 2012b). In boreal summer, the Atlantic and Pacific
high-pressure systems extended more westward in the 1960s and 1970s than later. On interannual time
scales, variations in pressure centres are related to modes of climate variability. Trends in the indices that
capture the strength of these modes are reported in Section 2.7.8, their characteristics and impacts are
discussed in Chapter 14.
[INSERT FIGURE 2.37 HERE]
Figure 2.37: Decadal averages of Sea Level Pressure (SLP) from the 20th century Reanalysis (20CR) for (left)
November of previous year to April and (right) May to October shown by two selected contours: 1004 hPa (dashed
lines) and 1020.5 hPa (solid lines). Topography above 2 km above mean sea level in 20CR is shaded in dark grey.
In summary, sea level pressure has likely decreased from 1979 to 2012 over the tropical Atlantic and
increased over large regions of the Pacific and South Atlantic, but trends are sensitive to the time period
analysed due to large decadal variability.
2.7.2
Surface Wind Speed
AR4 concluded that mid-latitude westerly winds have generally increased in both hemispheres. Because of
shortcomings in the observations, SREX stated that confidence in surface wind trends is low. Further studies
assessed here confirm this assessment.
Surface wind measurements over land and ocean are based on largely separate observing systems. Early
marine observations were based on ship speed through the water or sails carried or on visual estimates of sea
state converted to the wind speed using the Beaufort scale. Anemometer measurements were introduced
starting in the 1950s. The transition from Beaufort to measured winds introduced a spurious trend,
compounded by an increase in mean anemometer height over time (Kent et al., 2007; Thomas et al., 2008).
ICOADS release 2.5 (Woodruff et al., 2011) contains information on measurement methods and wind
measurement heights, permitting adjustment for these effects. The ICOADS-based dataset WASWind
(1950–2010; Tokinaga and Xie, 2011a) and the interpolated product NOCS v.2.0 (1973-present; Berry and
Kent, 2011) include such corrections, among other improvements.
Marine surface winds are also measured from space using various microwave range instruments:
scatterometers and synthetic aperture radars retrieve wind vectors, while altimeters and passive radiometers
measure wind speed only (Bourassa et al., 2010). The latter type provides the longest continuous record,
starting in July 1987. Satellite-based interpolated marine surface wind datasets use objective analysis
methods to blend together data from different satellites and atmospheric reanalyses. The latter provide wind
directions as in Blended Sea Winds (BSW, Zhang et al., 2006), or background fields as in Cross-Calibrated
Multi-Platform winds (CCMP, Atlas et al., 2011) and OAFlux (Yu and Weller, 2007). CCMP uses additional
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dynamical constraints, in situ data and a recently homogenized dataset of SSM/I observations (Wentz et al.,
2007), among other satellite sources.
Figure 2.38 compares 1988–2010 linear trends in surface wind speeds from interpolated datasets based on
satellite data, from interpolated and non-interpolated datasets based on in situ data, and from atmospheric
reanalyses. Note that these trends over a 23-year-long period primarily reflect decadal variability in winds,
rather than long-term climate change (Box 2.2). Kent et al. (2012) recently intercompared several of these
datasets and found large differences. The differences in trend patterns in Figure 2.38 are large as well.
Nevertheless, some statistically significant features are present in most datasets, including a pattern of
positive and negative trend bands across the North Atlantic Ocean (Section 2.7.6.2.) and positive trends
along the west coast of North America. Strengthening of the Southern Ocean winds, consistent with the
increasing trend in the SAM (Section 2.7.8) and with the observed changes in wind stress fields described in
Section 3.4.4, can be seen in satellite-based analyses and atmospheric reanalyses in Figure 2.38. Alternating
Southern Ocean trend signs in the NOCS v.2.0 panel are due to interpolation of very sparse in situ data (cf.
the panel for the uninterpolated WASWind product).
Surface winds over land have been measured with anemometers on a global scale for decades, but until
recently the data have been rarely used for trend analysis. Global datasets lack important meta information
on instrumentation and siting (McVicar et al., 2012). Long, homogenized instrumental records are rare (e.g.,
Usbeck et al., 2010; Wan et al., 2010). Moreover, wind speed trends are sensitive to the anemometer height
(Troccoli et al., 2012). Winds near the surface can be derived from reanalysis products (Box 2.3), but
discrepancies are found when comparing trends therein with trends for land stations (Smits et al., 2005;
McVicar et al., 2008).
Over land, a weakening of seasonal and annual mean as well as maximum winds is reported for many
regions from around the 1960s or 1970s to the early 2000s (a detailed review is given in McVicar et al.
(2012)), including China and the Tibetan region (Xu et al., 2006b; Guo et al., 2010) (but levelling off since
2000; Lin et al., 2012), western and southern Europe (e.g., Earl et al., 2013), much of the USA (Pryor et al.,
2007), Australia (McVicar et al., 2008), and southern and western Canada (Wan et al., 2010). Increasing
wind speeds were found at high latitudes in both hemispheres, namely in Alaska from 1921 to 2001 (Lynch
et al., 2004), in the central Canadian Arctic and Yukon from the 1950 to the 2000s (Wan et al., 2010), and in
coastal Antarctica over the second half of the 20th century (Turner et al., 2005). A global review of 148
studies showed that near-surface terrestrial wind speeds are declining in the Tropics and the mid latitudes of
both hemispheres at a rate of −0.14 m s−1 per decade (McVicar et al., 2012). Vautard et al. (2010), analysing
a global land surface wind dataset from 1979 to 2008, found negative trends on the order of –0.1 m s–1 per
decade over large portions of Northern Hemispheric land areas. The wind speed trend pattern over land
inferred from their data (1988–2010, Figure 2.38) has many points with magnitudes much larger than those
in the reanalysis products, which appear to underestimate systematically the wind speed over land, as well as
in coastal regions (Kent et al., 2012).
[INSERT FIGURE 2.38 HERE]
Figure 2.38: Trends in surface wind speed for 1988–2010. Shown in the top row are datasets based on the satellite
wind observations: (a) Cross-Calibrated Multi-Platform wind product (CCMP, Atlas et al., 2011); (b) wind speed from
the Objectively Analyzed Air-Sea Heat Fluxes dataset, release 3 (OAFlux); (c) Blended Sea Winds (BSW, Zhang et al.,
2006); in the middle row are datasets based on surface observations: (d) wind speed from the Surface Flux Dataset, v.2,
from NOC, Southampton, U.K. (Berry and Kent, 2009); (e) Wave- and Anemometer-based Sea Surface Wind
(WASWind, (Tokinaga and Xie, 2011a)); (f) Surface Winds on the Land (Vautard et al., 2010); and in the bottom row
are surface wind speeds from atmospheric reanalyses: (g) ERA-Interim; (h) NCEP-NCAR, v.1 (NNR); and (i) 20th
century Reanalysis (20CR, Compo et al., 2011). Wind speeds correspond to 10 m heights in all products. Land station
winds (panel f) are also for 10 m (but anemometer height is not always reported) except for the Australian data where
they correspond to 2 m height. To improve readability of plots, all datasets (including land station data) were averaged
to the 4° × 4° uniform longitude-latitude grid. Trends were computed for the annually averaged timeseries of 4° × 4°
cells. For all datasets except land station data, an annual mean was considered available only if monthly means for no
less than eight months were available in that calendar year. Trend values were computed only if no less than 17 years
had values and at least one year was available among the first and last three years of the period. White areas indicate
incomplete or missing data. Black plus signs (+) indicate grid boxes where trends are significant (i.e., a trend of zero
lies outside the 90% confidence interval).
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In summary, confidence is low in changes in surface wind speed over the land and over the oceans due to
remaining uncertainties in datasets and measures used.
2.7.3
Upper-Air Winds
In contrast to surface winds, winds above the planetary boundary layer have received little attention in AR4.
Radiosondes and pilot balloon observations are available from around the 1930s (Stickler et al., 2010).
Temporal inhomogeneities in radiosonde wind records are less common, but also less studied, than those in
radiosonde temperature records (Gruber and Haimberger, 2008; Section 2.4.4.3). Upper air winds can also be
derived from tracking clouds or water vapour in satellite imagery (Menzel, 2001) or from measurements
using wind profilers, air craft, or thermal observations, all of which serve as an input to reanalyses (Box 2.3).
In the past few years, interest in an accurate depiction of upper air winds has grown, since they are essential
for estimating the state and changes of the general atmospheric circulation and for explaining changes in the
surface winds (Vautard et al., 2010). Allen and Sherwood (2008), analysing wind shear from radiosonde
data, found significant positive zonal mean wind trends in the northern extratropics in the upper troposphere
and stratosphere and negative trends in the tropical upper troposphere for the period 1979–2005. Vautard et
al. (2010) find increasing wind speed in radiosonde observations in the lower and middle troposphere from
1979 to 2008 over Europe and North America and decreasing wind speeds over Central and Eastern Asia.
However, systematic global trend analyses of radiosonde winds are rare, prohibiting an assessment of upperair wind trends (specific features such as monsoons, jet streams and storms are discussed in Sections 2.7.5,
2.7.6 and 2.6, respectively).
In summary, upper-air winds are less studied than other aspects of the circulation, and less is known about
the quality of data products, hence confidence in upper-air wind trends is low.
2.7.4
Tropospheric Geopotential Height and Tropopause
AR4 concluded that over the NH between 1960 and 2000, boreal winter and annual means of tropospheric
GPH decreased over high latitudes and increased over the mid-latitudes. AR4 also reported an increase in
tropical tropopause height and a slight cooling of the tropical cold-point tropopause.
Changes in GPH, which can be addressed using radiosonde data or reanalysis data (Box 2.3), reflect SLP and
temperature changes in the atmospheric levels below. The spatial gradients of the trend indicate changes in
the upper-level circulation. As for SLP, tropopsheric GPH trends strongly depend on the period analysed due
to pronounced decadal variability. For the 1979–2012 period, trends for 500 hPa GPH from the ERA-Interim
reanalysis (Figure 2.36) as well as for other reanalyses show a significant decrease only at southern high
latitudes in November to April, but significant positive GPH trends in the subtropics and northern high
latitudes. Hence the change in the time period leads to a different trend pattern as compared to AR4. The
seasonality and spatial dependence of 500 hPa GPH trends over Antarctica was highlighted by Neff et al.
(2008), based upon radiosonde data over the period 1957–2007.
Minimum temperatures near the tropical tropopause (and therefore tropical tropopause height) are important
as they affect the water vapour input into the stratosphere (Section 2.2.2.1). Studies since AR4 confirm the
increase in tropopause height (Wang et al., 2012c). For tropical tropopause temperatures, studies based on
radiosonde data and reanalyses partly support a cooling between the 1990s and the early 2000s (Randel et
al., 2006; Randel and Jensen, 2013), but uncertainties in long-term trends of the tropical cold-point
tropopause temperature from radiosondes (Wang et al., 2012c; Randel and Jensen, 2013) and reanalyses
(Gettelman et al., 2010) are large and confidence is therefore low.
In summary, tropospheric geopotential height likely decreased from 1979 to 2012 at southern high latitudes
in austral summer and increased in the subtropics and northern high latitudes. Confidence in trends of the
tropical cold-point tropopause is low due to remaining uncertainties in the data.
2.7.5
Tropical Circulation
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In AR4, large interannual variability of the Hadley and Walker circulation was highlighted, as well as the
difficulty in addressing changes in these features in the light of discrepancies between datasets. AR4 also
found that rainfall in many monsoon systems exhibits decadal changes, but that data uncertainties restrict
confidence in trends. SREX also attributed low confidence to observed trends in monsoons.
Observational evidence for trends and variability in the strength of the Hadley and Walker circulations
(Annex III: Glossary), the monsoons, and the width of the tropical belt is based on radiosonde and reanalyses
data (Box 2.3). Additionally, changes in the tropical circulation imprint on other fields that are observed
from space (e.g., total ozone, outgoing long-wave radiation). Changes in the average state of the tropical
circulation are constrained to some extent by changes in the water cycle (Held and Soden, 2006; Schneider et
al., 2010). Changes in the monsoon systems are expressed through altered circulation, moisture transport and
convergence, and precipitation. Only a few monsoon studies address circulation changes, while most work
focuses on precipitation.
Several studies report a weakening of the global monsoon circulations as well as a decrease of global land
monsoon rainfall or of the number of precipitation days over the past 40–50 years (Zhou et al., 2008, see also
SREX; Liu et al., 2011). Concerning the East Asian Monsoon, a year-round decrease is reported for wind
speeds over China at the surface and in the lower troposphere based on surface observations and radiosonde
data (Guo et al., 2010; Jiang et al., 2010; Vautard et al., 2010; Xu et al., 2010). The changes in wind speed
are concomitant with changes in pressure centres such as a westward extension of the Western Pacific
Subtropical High (Gong and Ho, 2002; Zhou et al., 2009b). A weakening of the East Asian summer
monsoon since the 1920s is also found in SLP gradients (Zhou et al., 2009a). However, trends derived from
wind observations and circulation trends from reanalysis data carry large uncertainties (Figure 2.38), and
monsoon rainfall trends depend, e.g., on the definition of the monsoon area (Hsu et al., 2011). For instance,
usig a new definition of monsoon area, an increase in northern hemispheric and global summer monsoon
(land and ocean) precipitation is reported from 1979 to 2008 (Hsu et al., 2011; Wang et al., 2012a).
The additional datasets that became available since AR4 confirm the large interannual variability of the
Hadley and Walker circulation. The strength of the northern Hadley circulation (Figure 2.39) in boreal
winter and of the Pacific Walker circulation in boreal fall and winter is largely related to the ENSO (Box
2.5). This association dominates interannual variability and affects trends. Datasets do not agree well with
respect to trends in the Hadley circulation (Figure 2.39). Two widely used reanalysis datasets, NNR and
ERA-40, both have demonstrated shortcomings with respect to tropical circulation; hence their increases in
the Hadley circulation strength since the 1970s might be artificial (Mitas and Clement, 2005; Song and
Zhang, 2007; Hu et al., 2011; Stachnik and Schumacher, 2011). Later generation reanalysis datasets
including ERA-Interim (Bronnimann et al., 2009; Nguyen et al., 2013) as well as satellite humidity data
(Sohn and Park, 2010) also suggest a strengthening from the mid 1970s to present, but the magnitude is
strongly dataset dependent.
[INSERT FIGURE 2.39 HERE]
Figure 2.39: a) Indices of the strength of the northern Hadley circulation in December to March (Ψmax is the maximum
of the meridional mass stream function at 500 hPa between the equator and 40°N. b) Indices of the strength of the
Pacific Walker circulation in September to January (Δω is the difference in the vertical velocity between [10°S to 10°N,
180°W to 100°W] and [10°S to 10°N, 100°E to 150°E] as in Oort and Yienger (1996), Δc is the difference in cloud
cover between [6°N–12°S, 165°E–149°W] and [18°N–6°N, 165°E–149°W] as in Deser et al. (2010a), vE is the effective
wind index from SSM/I satellite data, updated from Sohn and Park (2010), u is the zonal wind at 10 m averaged in the
region [10°S–10°N, 160°E–160°W], ΔSLP is the SLP difference between [5°S–5°N, 160°W–80°W] and [5°S–5°N,
80°E–160°E] as in Vecchi et al. (2006)). Reanalysis datasets include 20CR, NCEP/NCAR, ERA-Interim, JRA-25,
MERRA, and CFSR, except for the zonal wind at 10 m (20CR, NCEP/NCAR, ERA-Interim), where available until
January 2013. ERA-40 and NCEP2 are not shown as they are outliers with respect to the strength trend of the northern
Hadley circulation (Mitas and Clement, 2005; Song and Zhang, 2007; Hu et al., 2011; Stachnik and Schumacher, 2011).
Observation datasets include HadSLP2 (Section 2.7.1), ICOADS (Section 2.7.2, only 1957–2009 data are shown) and
WASWIND (Section 2.7.2), reconstructions are from Broennimann et al. (2009). Where more than one time series was
available, anomalies from the 1980/1981 to 2009/2010 mean values of each series are shown.
Consistent changes in different observed variables suggest a weakening of the Pacific Walker circulation
during much of the 20th century that has been largely offset by a recent strengthening. A weakening is
indicated by trends in the zonal SLP gradient across the equatorial Pacific (Section 2.7.1, Table 2.14) from
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1861 to 1992 (Vecchi et al., 2006), or from 1901 to 2004 (Power and Kociuba, 2011b). Boreal spring and
summer contribute most strongly to the centennial trend (Nicholls, 2008; Karnauskas et al., 2009), as well as
to the trend in the second half of the 20th century (Tokinaga et al., 2012). For boreal fall and winter, when
the circulation is strongest, no trend is found in the Pacific Walker circulation based on the vertical velocity
at 500 hPa from reanalyses (Compo et al., 2011), equatorial Pacific 10 m zonal winds, or SLP in Darwin
(Nicholls, 2008; Figure 2.39). However, there are inconsistencies between ERA-40 and NNR (Chen et al.,
2008). Deser et al. (2010a) find changes in marine air temperature and cloud cover over the Pacific that are
consistent with a weakening of the Walker circulation during most of the 20th century (Section 2.5.7.1 and
Yu and Zwiers, 2010). Tokinaga et al. (2012) find robust evidence for a weakening of the Walker circulation
(most notably over the Indian Ocean) from 1950 to 2008 based on observations of cloud cover, surface wind,
and SLP. Since the 1980s or 1990s, however, trends in the Pacific Walker circulation have reversed (Figure
2.39; Luo et al., 2012). This is evident from changes in SLP (see equatorial SOI trends in Table 2.14 and
Box 2.5, Figure 1), vertical velocity (Compo et al., 2011), water vapour flux from satellite and reanalysis
data (Sohn and Park, 2010), or sea level height (Merrifield, 2011). It is also consistent with the SST trend
pattern since 1979 (Meng et al., 2012, see also Figure 2.22).
Observed changes in several atmospheric parameters suggest that the width of the tropical belt has increased
at least since 1979 (Seidel et al., 2008; Forster et al., 2011; Hu et al., 2011). Since AR4, wind, temperature,
radiation, and ozone information from radiosondes, satellites, and reanalyses had been used to diagnose the
tropical belt width and estimate their trends. Annual mean time series of the tropical belt width from various
sources are shown in Figure 2.40.
[INSERT FIGURE 2.40 HERE]
Figure 2.40: Annual average tropical belt width (left) and tropical edge latitudes in each hemisphere (right). The
tropopause (red), Hadley cell (blue), and jet stream (green) metrics are based on reanalyses (NCEP/NCAR, ERA-40,
JRA25, ERA-Interim, CFSR, and MERRA, see Box 2.3); outgoing longwave radiation (orange) and ozone (black)
metrics are based on satellite measurements. The ozone metric refers to equivalent latitude (Hudson et al., 2006;
Hudson, 2012). Adapted and updated from Seidel et al. (2008) using data presented in Davis and Rosenlof (2011) and
Hudson (2012). Where multiple datasets are available for a particular metric, all are shown as light solid lines, with
shading showing their range and a heavy solid line showing their median.
Since 1979 the region of low column ozone values typical of the tropics has expanded in the NH (Hudson et
al., 2006; Hudson, 2012). Based on radiosonde observations and reanalyses, the region of the high tropical
tropopause has expanded since 1979, and possibly since 1960 (Seidel and Randel, 2007; Birner, 2010; Lucas
et al., 2012), although widening estimates from different reanalyses and using different methodologies show
a range of magnitudes (Seidel and Randel, 2007; Birner, 2010).
Several lines of evidence indicate that climate features at the edges of the Hadley cell have also moved
poleward since 1979. Subtropical jet metrics from reanalysis zonal winds (Strong and Davis, 2007; Archer
and Caldeira, 2008b, 2008a; Strong and Davis, 2008) and layer-average satellite temperatures (Fu et al.,
2006; Fu and Lin, 2011) also indicate widening, although 1979–2009 wind-based trends (Davis and
Rosenlof, 2011) are not statistically significant. Changes in subtropical outgoing longwave radiation, a
surrogate for high cloud, also suggest widening (Hu and Fu, 2007), but the methodology and results are
disputed (Davis and Rosenlof, 2011). Widening of the tropical belt is also found in precipitation patterns (Hu
and Fu, 2007; Davis and Rosenlof, 2011; Hu et al., 2011; Kang et al., 2011; Zhou et al., 2011), including in
SH regions (Cai et al., 2012).
The qualitative consistency of these observed changes in independent datasets suggests a widening of the
tropical belt between at least 1979 and 2005 (Seidel et al., 2008), and possibly longer. Widening estimates
range between around 0° and 3° latitude per decade, but their uncertainties have been only partially explored
(Birner, 2010; Davis and Rosenlof, 2011).
In summary, large interannual-to-decadal variability is found in the strength of the Hadley and Walker
circulation. The confidence in trends in the strength of the Hadley circulation is low due to uncertainties in
reanalysis datasets. Recent strengthening of the Pacific Walker circulation has largely offset the weakening
trend from the 19th century to the 1990s. Several lines of independent evidence indicate a widening of the
tropical belt since the 1970s. The suggested weakening of the East Asian monsoon has low confidence, given
the nature and quality of the evidence.
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2.7.6
2.7.6.1
Chapter 2
IPCC WGI Fifth Assessment Report
Jets, Storm Tracks and Weather Types
Midlatitude and Subtropical Jets and storm track position
AR4 reported a poleward displacement of Atlantic and southern polar front jet streams from the 1960s to at
least the mid-1990s and a poleward shift of the northern hemispheric storm tracks. However, it was also
noted that uncertainties are large and that NNR and ERA-40 disagree in important aspects. SREX also
reported a poleward shift of NH and SH storm tracks. Studies since AR4 confirm that in the NH, the jet core
has been migrating towards the pole since the 1970s, but trends in the jet speed are uncertain. Additional
studies assessed here further support the poleward shift of the North Atlantic storm track from the 1950s to
the early 2000s.
Subtropical and midlatitude jet streams are three-dimensional entities that vary meridionally, zonally, and
vertically. The position of the midlatitude jet streams is related to the position of the midlatitude storm
tracks; regions of enhanced synoptic activity due to the passage of cyclones (Section 2.6). Jet stream winds
can be determined from radiosonde measurements of GPH using quasi-geostrophic flow assumptions. Using
reanalysis datasets (Box 2.3), it is possible to track 3-dimensional jet variations by identifying a surface of
maximum wind (SMW), although a high vertical resolution is required for identification of jets.
Various new analyses based on NCEP/NCAR and ERA-40 reanalyses as well as MSU/AMSU lower
stratospheric temperatures (Section 2.4.4) confirm that the jet streams (midlatitude and subtropical) have
been moving poleward in most regions in the NH over the last three decades (Fu et al., 2006; Hu and Fu,
2007; Strong and Davis, 2007; Archer and Caldeira, 2008a; Fu and Lin, 2011) but no clear trend is found in
the SH (Swart and Fyfe, 2012). There is inconsistency with respect to jet speed trends based upon whether
one uses an SMW-based or isobaric-based approach (Strong and Davis, 2007; Archer and Caldeira, 2008b,
2008a; Strong and Davis, 2008) and the choice of analysis periods due to inhomogeneities in reanalyses
(Archer and Caldeira, 2008a). In general, jets have become more common (and jet speeds have increased)
over the western and central Pacific, eastern Canada, the North Atlantic and Europe (Strong and Davis,
2007; Barton and Ellis, 2009), trends that are concomitant with regional increases in GPH gradients and
circumpolar vortex contraction (Frauenfeld and Davis, 2003; Angell, 2006). From a climate dynamics
perspective, these trends are driven by regional patterns of tropospheric and lower stratospheric warming or
cooling and thus are coupled to large-scale circulation variability.
The North Atlantic storm track is closely associated with the NAO (Schneidereit et al., 2007). Studies based
on ERA-40 reanalysis (Schneidereit et al., 2007), SLP measurements from ships (Chang, 2007), sea level
time series (Vilibic and Sepic, 2010), and cloud analyses (Bender et al., 2012) support a poleward shift and
intensification of the North Atlantic cyclone tracks from the 1950s to the early 2000s (Sorteberg and Walsh,
2008; Cornes and Jones, 2011).
2.7.6.2
Weather Types and Blocking
In AR4, weather types were not assessed as such, but an increase in blocking frequency in the Western
Pacific and a decrease in North Atlantic were noted.
Changes in the frequency of weather types are of interest since weather extremes are often associated with
specific weather types. For instance, persistent blocking of the westerly flow was essential in the
development of the 2010 heat wave in Russia (Dole et al., 2011) (Sections 9.5.2.2. and Box 14.2). Synoptic
classifications or statistical clustering (Philipp et al., 2007) are commonly used to classify the weather on a
given day. Feature-based methods are also used (Croci-Maspoli et al., 2007a). All these methods require
daily SLP or upper-level fields.
Trends in synoptic weather types have been best analysed for Central Europe since the mid-20th century,
where several studies describe an increase in westerly or cyclonic weather types in winter but an increase of
anticyclonic, dry weather types in summer (Philipp et al., 2007; Werner et al., 2008; Trnka et al., 2009). An
eastward shift of blocking events over the North Atlantic (fewer blocking over Greenland and more frequent
blocking over the eartern North Atlantic) and the North Pacific was found by Davini et al. (2012) using
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NCEP/NCAR reanalysis since 1951 and by Croci-Maspoli et al. (2007a) in ERA-40 reanalysis during the
period 1957–2001. Mokhov et al. (2013) find an increase in blocking duration over the NH year-round since
about 1990 in a study based on NCEP/NCAR reanalysis data from 1969–2011. For the SH, Dong et al.
(2008) found a decrease in number of blocking days but increase in intensity of blocking over the period
1948–1999. Differences in blocking index definitions, the sensitivity of some indices to changes in the mean
field, and strong interannual variability in all seasons (Kreienkamp et al., 2010), partly related to circulation
variability modes (Croci-Maspoli et al., 2007b), complicate a global assessment of blocking trends.
In summary, there is evidence for a poleward shift of storm tracks and jet streams since the 1970s. Based on
the consistency of these trends with the widening of the tropical belt (Section 2.7.5), trends that are based on
many different datasets, variables, and approaches, it is likely that circulation features have moved poleward
since the 1970s. Methodological differences between studies mean there is low confidence in characterizing
the global nature of any change in blocking.
2.7.7
Stratospheric Circulation
Changes in the polar vortices were assessed in AR4. A significant decrease in lower-stratospheric GPH in
summer over Antarctica since 1969 was found, whereas trends in the Northern Polar Vortex were considered
uncertain due to its large variability.
The most important characteristics of the stratospheric circulation for climate and for trace gas distribution
are the winter and spring polar vortices and Sudden Stratospheric Warmings (rapid warmings of the middle
stratosphere that may lead to a collapse of the Polar Vortex), the Quasi-Biennial Oscillation (an oscillation of
equatorial zonal winds with a downward phase propagation), and the Brewer-Dobson circulation (BDC, the
meridional overturning circulation transporting air upward in the tropics, poleward to the winter hemisphere,
and downward at polar and subpolar latitudes; Annex III: Glossary). Radiosonde observations, reanalysis
datasets, and space-borne temperature or trace gas observations are used to address changes in the
stratospheric circulation, but all of these sources of information carry large trend uncertainties.
The AR4 assessment was further corroborated in Forster et al. (2011) and in updated 100 hPa GPH trends
from ERA-Interim reanalysis (Box 2.3, Figure 2.36). There is high confidence that lower stratospheric GPH
over Antarctica has decreased in spring and summer at least since 1979. Cohen et al. (2009) reported an
increase in the number of Arctic sudden stratospheric warmings during the last two decades. However,
interannual variability in the Arctic Polar Vortex is large, uncertainties in reanalysis products are high
(Tegtmeier et al., 2008), and trends depend strongly on the time period analysed (Langematz and Kunze,
2008).
The BDC is only indirectly observable via wave activity diagnostics (which represent the main driving
mechanism of the BDC), via temperatures or via the distribution of trace gases, which may allow the
determination of the ‘age of air’ (i.e., the time an air parcel has resided in the stratosphere after its entry from
the troposphere). Randel et al. (2006), found a sudden decrease in global lower stratospheric water vapour
and ozone around 2001 that is consistent with an increase in the mean tropical upwelling, i.e., the tropical
branch of the BDC (Rosenlof and Reid, 2008; Section 2.2.2.1; Lanzante, 2009; Randel and Jensen, 2013).
On the other hand, Engel et al. (2009) found no statistically significant change in the age of air in the 24-35
km layer over the NH midlatitudes from measurements of chemically inert trace gases from 1975 to 2005.
However, this does not rule out trends in the lower stratospheric branch of the BDC or trends in mid to low
latitude mixing (Bonisch et al., 2009; Ray et al., 2010). All of these methods are subject to considerable
uncertainties, and they might shed light only on some aspects of the BDC. Confidence in trends in the BDC
is therefore low.
In summary, it is likely that lower-stratopheric geopotential height over Antarctica has decreased in spring
and summer at least since 1979. Due to uncertainties in the data and approaches used, confidence in trends in
the Brewer-Dobson circulation is low.
2.7.8
Changes in Indices of Climate Variability
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AR4 assessed changes in indices of climate variability. The NAO and SAM were found to exhibit positive
trends (strengthened midlatitude westerlies) from the 1960s to 1990s, but the NAO has returned to its longterm mean state since then.
Indices of climate variability describe the state of the climate system with regards to individual modes of
climate variability. Together with corresponding spatial patterns, they summarize large fractions of spatiotemporal climate variability. Inferences about significant trends in indices are generally hampered by relative
shortness of climate records, their uncertainties, and the presence of large variability on decadal and
multidecadal time scales.
Table 2.14 summarizes observed changes in well-known indices of climate variability (see Box 2.5, Table 1
for precise definitions). Even the indices that explicitly include detrending of the entire record (e.g., Deser et
al., 2010b), can exhibit statistically significant trends over shorter sub-periods. Confidence intervals in Table
2.14 that do not contain zero indicate trend significance at 10% level; however, the trends significant at 5%
and 1% level are emphasized in the discussion below. Chapter 14 discusses the main features and physical
meaning of individual climate modes.
The NAO index reached very low values in the winter of 2010 (Osborn, 2011). As a result, with the
exception of the PC-based NAO index, which still shows a 5%-significant positive trend from 1951 to
present, other NAO or NAM indices do not show significant trends of either sign for the periods presented in
Table 2.14. In contrast, the SAM maintained the upward trend (Table 2.14). Fogt et al. (2009) found a
positive trend in the SAM index from 1957 to 2005. Visbeck (2009), in a station-based index, found an
increase in recent decades (1970s to 2000s).
The observed detrended multidecadal SST anomaly averaged over the North Atlantic Ocean area is often
called Atlantic Multidecadal Oscillation Index (AMO, see Box 2.5, Table 1, Figure 1). The warming trend in
the “revised” AMO index since 1979 is significant at 1% level (Table 2.14) but cannot be readily interpreted
because of the difficulty with reliable removal of the SST warming trend from it (Deser et al., 2010b).
On decadal and inter-decadal timescales the Pacific climate shows an irregular oscillation with long periods
of persistence in individual stages and prominent shifts between them. PDO, IPO and NPI indices
characterize this variability for both hemispheres and agree well with each other (Box 2.5, Figure 1). While
AR4 noted climate impacts of the 1976–1977 PDO phase transition, the shift in the opposite direction, both
in PDO and IPO, may have occurred at the end of 1990s (Cai and van Rensch, 2012; Dai, 2012).
Significance of 1979–2012 trends in PDO and NPI then would be an artefact of this change; incidentally, no
significant trends in these indices was seen for longer periods (Table 2.14). Nevertheless, Pacific changes
since the 1980s (positive for NPI and negative for PDO and IPO) are consistent with the observed sea level
pressure changes (Section 2.7.1) and with reversing trends in the Walker Circulation (Section 2.7.5),
reported to be slowing down during much of the 20th century but sped up again since the 1990s. Equatorial
SOI shows an increasing trend since 1979 at 1% significance; more traditionally defined SOI indices do not
show significant trends (Table 2.14).
NINO3.4 and NINO3 show a century-scale warming trend significant at 5% level, if computed from the
ERSSTv3b dataset (Section 2.4.2) but not if calculated from other datasets (Table 2.14). Furthermore, the
sign (and significance) of the trend in east-west SST gradient across the Pacific remains ambiguous (Vecchi
and Soden, 2007; Bunge and Clarke, 2009; Karnauskas et al., 2009; Deser et al., 2010a) (Section 14.4.1).
Table 2.14: Trends for selected indices listed in Box 2.5, Table 1. Each index was standardized for its
longest available period contained within the 1870–2012 interval. Standardization was done on the DJFM
means for the NAO, NAM, and PNA, on seasonal anomalies for PSA1,2, and on monthly anomalies for all
other indices. Standardized monthly and seasonal anomalies were further averaged to annual means. Trend
values computed for annual or DJFM means are given in standard deviation per decade with their 90%
confidence intervals. Index records where the source is not explicitly indicated were computed from either
HadISST1 (for SST-based indices), or HadSLP2r (for SLP-based indices), or NNR fields of 500 hPa or 850
hPa geopotential height. CoA stands for ‘Centers of Action’ index definitions. Linear trends for 1870–2012
were removed from ATL3, BMI, and DMI.
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Chapter 2
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Trends in standard deviation units per decade
Index Name
(–1)*SOI from CPC
(–1)*SOI Troup from BOM records
SOI Darwin from BOM records
(–1)*EQSOI
NINO34
NINO34 (ERSST v.3b)
NINO34 (COBE SST)
NINO3
NINO3 (ERSST v.3b)
NINO3 (COBE SST)
NINO4
EMI
(–1)*TNI
PDO from Mantua et al. (1997)
(–1)*NPI
AMO revised
NAO stations from Jones et al (1997)
NAO stations from Hurrell (1995)
NAO PC from Hurrell (1995)
NAM PC
SAM Z850 PC
SAM SLP grid 40°S–70°S
SAM SLP stations from Marshall (2003)
PNA CoA
PNA RPC from CPC
PSA Karoly (1989) CoA definition
(–1)*PSA Yuan and Li (2008) CoA definition
PSA1 PC
PSA2 PC
ATL3
AONM PC
AMM PC
IOBM PC
BMI
IODM PC
DMI
Notes:
(a) Trend values significant at the 5% level
(b) Trend values significant at the 1% level
1901–2012
1951–2012
0.004 ± 0.103
0.018 ± 0.104
0.082 ± 0.085
–0.076 ± 0.143
0.012 ± 0.105
0.054 ± 0.103
0.008 ± 0.107
0.043 ± 0.095
0.098 ± 0.092
1979–2012
–0.243 ± 0.233
–0.247 ± 0.236
–0.116 ± 0.195
–0.558b ± 0.297
–0.156 ± 0.274
–0.085 ± 0.259
–0.154 ± 0.289
–0.143 ± 0.256
–0.073 ± 0.236
0.139b ± 0.026
0.054 ± 0.096
0.068 ± 0.145
–0.119 ± 0.189
0.066 ± 0.167
0.112 ± 0.189
0.010 ± 0.046
–0.012 ± 0.341
0.095 ± 0.149
0.171 ± 0.179
0.198a ± 0.148
0.141 ± 0.123
0.268b ± 0.063
0.198b ± 0.052
0.035 ± 0.043
0.064a ± 0.051
0.019 ± 0.058
0.075a ± 0.051
0.072a ± 0.050
–0.016 ± 0.034
0.030 ± 0.033
0.113 ± 0.114
0.202b ± 0.111
–0.267b ± 0.079
–0.211b ± 0.069
–0.163a ± 0.103
0.200b ± 0.066
0.125a ± 0.088
0.138a ± 0.109
–0.015 ± 0.155
0.314b ± 0.082
0.294b ± 0.083
–0.031 ± 0.093
0.080 ± 0.090
–0.113 ± 0.258
–0.102 ± 0.380
–0.131 ± 0.580
0.030 ± 0.550
–0.460a ± 0.284
–0.169a ± 0.105
0.779b ± 0.291
–0.136 ± 0.394
–0.214 ± 0.400
–0.037 ± 0.401
0.029 ± 0.360
0.100 ± 0.109
0.294b ± 0.131
0.128a ± 0.097
–0.103 ± 0.298
0.019 ± 0.271
–0.233a ± 0.174
–0.208 ± 0.189
–0.368a ± 0.245
0.036 ± 0.156
0.186 ± 0.193
0.327a ± 0.230
0.309 ± 0.324
0.201 ± 0.206
0.189 ± 0.206
–0.052 ± 0.203
0.211 ± 0.210
0.012 ± 0.039
0.028 ± 0.036
0.001 ± 0.051
–0.003 ± 0.042
0.067a ± 0.045
0.024 ± 0.041
0.007 ± 0.039
0.069b ± 0.039
0.034 ± 0.036
0.026 ± 0.054
–0.059 ± 0.061
0.019 ± 0.052
–0.017 ± 0.071
–0.026a ± 0.022
–0.001 ± 0.111
–0.044 ± 0.056
–0.001 ± 0.066
0.012 ± 0.059
0.003 ± 0.048
In addition to changes in the mean values of climate indices, changes in the associated spatial patterns are
also possible. In particular, the diversity of detail of different ENSO events and possible distinction between
their “flavors” have received significant attention (Section 14.4.2). These efforts also intensified the
discussion of useful ENSO indices in the literature. Starting from the work of Trenberth and Stepaniak
(2001), who proposed to characterize the evolution of ENSO events with the Trans-Niño Index (TNI), which
is virtually uncorrelated with the standard ENSO index NINO3.4, other alternative ENSO indices have been
introduced and proposals were made for classifying ENSO events according to the indices they primarily
maximize. While a traditional, ‘canonical’ El Niño event type (Rasmusson and Carpenter, 1982) is viewed as
the ‘eastern Pacific’ type, some of the alternative indices purport to identify events that have central Pacific
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maxima and are called dateline El Niño (Larkin and Harrison, 2005), Modoki (Ashok et al., 2007), or Central
Pacific El Niño (Kao and Yu, 2009). However, no consensus has been reached regarding the appropriate
classification of ENSO events. Takahashi et al. (2011) and Ren and Jin (2011) have presented many of the
popular ENSO indices as elements in a two-dimensional linear space spanned by a pair of such indices.
ENSO indices that involve Central and Western Pacific SST (NINO4, EMI, TNI) show no significant trends.
Significant positive PNA trends and negative and positive trends in the first and second PSA modes
respectively are observed over the last 60 years (Table 2.14). However, the level of significance of these
trends depends on the index definition and on the dataset used. The positive trend in the Atlantic Ocean
‘Niño’ mode (AONM) index and in ATL3 are due to the east-intensified warming in the Tropical Atlantic
that causes the the weakening of the Atlantic equatorial cold tongue: these changes were noticed by
Tokinaga and Xie (2011b) with regards to the last 60-year period. The Indian Ocean Basin Mode (IOBM)
has a strong warming trend (significant at 1% since the middle of the 20th century). This phenomenon is
well-known (Du and Xie, 2008) and its consequences for the regional climate is a subject of active research
(Du et al., 2009; Xie et al., 2009).
In summary, large variability on interannual to decadal time scales and remaining differences between
datasets precludes robust conclusions on long-term changes in indices of climate variability. The increase in
the NAO index from the 1950s to the 1990s has reversed in more recent periods so that confidence in longterm trends is low. It is likely that the SAM index has become more positive since the 1950s.
[START BOX 2.5 HERE]
Box 2.5: Patterns and Indices of Climate Variability
Much of the spatial structure of climate variability can be described as a combination of ‘preferred’ patterns.
The most prominent of these are known as modes of climate variability and impact weather and climate on
many spatial and temporal scales (Chapter 14). Individual climate modes historically have been identified
through spatial teleconnections: correlations between regional climate variations at widely separated,
geographically fixed spatial locations. An index describing temporal variations of the climate mode in
question can be formed, e.g., by adding climate anomalies calculated from meteorological records at stations
exhibiting the strongest correlation with the mode and subtracting anomalies at stations exhibiting
anticorrelation. By regressing climate records from other places on this index, one derives a spatial climate
pattern characterizing this mode. Patterns of climate variability have also been derived using a variety of
mathematical techniques such as principal component analysis (PCA). These patterns and their indices are
useful both because they efficiently describe climate variability in terms of a few preferred modes and also
because they can provide clues about how the variablility is sustained (Box 14.1 provides formal definitions
of these terms).
Box 2.5, Table 1 lists some prominent modes of large-scale climate variability and indices used for defining
them. Changes in these indices are associated with large-scale climate variations on interannual and longer
time scales. With some exceptions, indices shown have been used by a variety of authors. They are defined
relatively simply from raw or statistically analyzed observations of a single climate variable, which has a
history of surface observations. For most of these indices at least a century-long record is available for
climate research.
[INSERT BOX 2.5, TABLE 1 HERE]
Box 2.5, Table 1: Established indices of climate variability with global or regional influence. Z500, Z700, and Z850
denote geopotential height at the 500, 700 and 850 hPa levels, respectively. Subscript s and a denote "standardized" and
"anomalies", respectively. Further information is given in Supplementary Material 2.SM.8. Climate impacts of these
modes are listed in Box 14.1.
Most climate modes are illustrated by several indices (Box 2.5, Figure 1), which often behave similarly to
each other. Spatial patterns of sea surface temperature (SST) or sea level pressure (SLP) associated with
these climate modes are illustrated in Box 2.5, Figure 2. They can be interpreted as a change in the SST or
SLP field associated with one standard deviation change in the index.
[INSERT BOX 2.5, FIGURE 1 HERE]
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Box 2.5, Figure 1: Some indices of climate variability, as defined in Box 2.5, Table 1, plotted in the 1870–2012
interval. Where ‘HadISST1’, ‘HadSLP2r’, or ‘NNR’ are indicated, the indices were computed from the Sea Surface
Temperature (SST) or Sea Level Pressure (SLP) values of the former two datasets or from 500 or 850 hPa geopotential
height fields from the NNR. Dataset references given in the panel titles apply to all indices shown in that panel. Where
no dataset is specified, a publicly available version of an index from the authors of a primary reference given in Box
2.5, Table 1 was used. All indices were standardized with regard to 1971–2000 period except for NINO3.4 (centralized
for 1971–2000) and AMO indices (centralized for 1901–1970). Indices marked as “detrended” had their linear trend for
1870–2012 removed. All indices are shown as 12-month running means except when the temporal resolution is
explicitly indicated (e.g., ‘DJFM’ for December-to-March averages) or smoothing level (e.g., 11-year LPF for a lowpass filter with half-power at 11 years).
[INSERT BOX 2.5, FIGURE 2 HERE]
Box 2.5, Figure 2: Spatial patterns of climate modes listed in Box 2.5, Table 1. All patterns shown here are obtained by
regression of either Sea Surface Temperature (SST) or Sea Level Pressure (SLP) fields on the standardized index of a
climate mode. For each climate mode one of the specific indices shown in Box 2.5, Figure 1 was used, as identified in
the panel subtitles. SST and SLP fields are from HadISST1 and HadSLP2r datasets (interpolated gridded products
based on datasets of historical observations). Regressions were done on monthly means for all patterns except for NAO
and PNA, which were done with the DJFM means, and for PSA1 and PSA2, where seasonal means were used. Each
regression was done for the longest period within the 1870-2012 interval when the index was available. For each pattern
the time series was linearly de-trended over the entire regression interval. All patterns are shown by color plots, except
for PSA2, which is shown by white contours over the PSA1 color plot (contour steps are 0.5 hPa, zero contour is
skipped, negative values are indicated by dash).
The difficulty of identifying a universally ‘best’ index for any particular climate mode is due to the fact that
no simply defined indicator can achieve a perfect separation of the target phenomenon from all other effects
occurring in the climate system. As a result, each index is affected by many climate phenomena whose
relative contributions may change with the time period and the dataset used. Limited length and quality of
the observational record further compound this problem. Thus the choice of index is always applicationspecific.
[END BOX 2.5 HERE]
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Tables
Table 2.1: Global annual mean surface dry-air mole fractions and their change since 2005 for well-mixed GHG from
four measurement networks. Units are ppt except where noted. Uncertainties are 90% confidence intervals. aREs
(radiative efficiency) and lifetimes (except CH4 and N2O, which are from Prather et al., 2012) are from Chapter 8.
Lifetime
RE
(yr)
(W m–2 ppb–1)
Species
2011 Global Annual Mean
SIOb/AGAG
E
NOAA
UCI
1.37  10–5
CO2 (ppm)
Global Increase from 2005 to 2011
UCI
390.48 ± 0.28 390.44 ± 0.16
SIOb/AGAGE
11.67 ± 0.37
NOAA
11.66 ± 0.13
CH4 (ppb)
9.1
3.63  10–4
N2O (ppb)
131
3.03  10–3
324.0 ± 0.1
324.3 ± 0.1
4.7 ± 0.2
5.24 ± 0.14
SF6
3200
0.575
7.26 ± 0.02
7.31 ± 0.02
1.65 ± 0.03
1.64 ±0.01
CF4
50000
0.1
79.0 ± 0.1
4.0 ± 0.2
C2F6
10000
0.26
4.16 ± 0.02
0.50 ± 0.03
HFC-125
28.2
0.219
9.58 ± 0.04
5.89 ± 0.07
HFC-134a
13.4
0.159
HFC-143a
47.1
0.159
12.04 ± 0.07
6.39 ± 0.10
HFC-152a
1.5
0.094
6.4 ± 0.1
3.0 ± 0.2
HFC-23
222
0.176
24.0 ± 0.3
5.2 ± 0.6
CFC-11
45
0.263
237.9 ± 0.8 236.9 ± 0.1
238.5 ± 0.2
–13.2 ± 0.8 –12.7 ± 0.2
–13.0 ± 0.1
CFC-12
100
0.32
525.3 ± 0.8 529.5 ± 0.2
527.4 ± 0.4
–12.8 ± 0.8 –13.4 ± 0.3
–14.1 ± 0.1
CFC-113
85
0.3
74.9 ± 0.6
HCFC-22
11.9
0.2
209.0 ± 1.2 213.4 ± 0.8
HCFC-141b 9.2
0.152
20.8 ± 0.5
HCFC-142b 17.2
0.186
CCl4
0.175
26
1798.1 ± 0.6 1803.1 ± 4.8 1803.2 ± 1.2 26.6 ± 0.9 28.9 ± 6.8
63.4 ± 0.9
62.4 ± 0.3
63.0 ± 0.6
27.7 ± 1.4 28.2 ± 0.4
74.29 ± 0.06 74.40 ± 0.04 –4.6 ± 0.8 –4.25 ± 0.08
213.2 ± 1.2
28.6 ± 0.9
28.2 ± 0.1
–4.35 ±0.02
41.5 ± 1.4 44.6 ± 1.1
44.3 ± 0.2
21.38 ± 0.09 21.4 ± 0.2
3.7 ± 0.5
3.70 ± 0.1
3.76 ± 0.03
21.0 ± 0.5
21.35 ± 0.06 21.0 ± 0.1
4.9 ± 0.5
5.72 ± 0.09
5.73 ± 0.04
87.8 ± 0.6
85.0 ± 0.1
–6.4 ± 0.5 –6.9 ± 0.2
86.5 ± 0.3
–7.8 ± 0.1
CH3CCl3
5
0.069
6.8 ± 0.6
6.3 ± 0.1
6.35 ± 0.07 –14.8 ± 0.5 –11.9 ± 0.2
–12.1 ± 0.1
Notes:
AGAGE = Advanced Global Atmospheric Gases Experiment; NOAA = National Oceanic and Atmospheric
Administration, Earth System Research Laboratory, Global Monitoring Division; SIO = Scripps Institution of
Oceanography, University of California, San Diego; UCI = University of California, Irvine, Department of Chemistry.
HFC-125 = CHF2CF3; HFC-134a = CH2FCF3; HFC-143a = CH3CF3; HFC-152a = CH3CHF2; HFC-23 = CHF3; CFC-11
= CCl3F; CFC-12 = CCl2F2; CFC-113 = CClF2CCl2F; HCFC-22 = CHClF2; HCFC-141b = CH3CCl2F; HCFC-142b =
CH3CClF2.
(a) Each program uses different methods to estimate uncertainties.
(b) SIO reports only CO2; all other values reported in these columns are from AGAGE. SIO CO2 program and AGAGE
are not affiliated with each other.
Budget lifetimes are shown; for CH4 and N2O, perturbation lifetimes (12.4 years for CH4 and 121 years for N2O) are
used to estimate global warming potentials (Chapter 8)
Year 1750 values determined from air extracted from ice cores are below detection limits for all species except CO2
(278 ± 2 ppm), CH4 (722 ± 25 ppb), N2O (270 ± 7 ppb) and CF4 (34.7 ± 0.2 ppt). Centennial variations up to 10 ppm
CO2, 40 ppb CH4, and 10 ppb occur throughout the late-Holocene (Chapter 6).
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IPCC WGI Fifth Assessment Report
Table 2.13: Regional observed changes in a range of climate indices since the middle of the 20th century. Assessments are based on a range of ‘global’ studies and assessments
(Groisman et al., 2005; Alexander et al., 2006; Caesar et al., 2006; Sheffield and Wood, 2008; Dai, 2011a; Dai, 2011b; Seneviratne et al., 2012; Sheffield et al., 2012; Dai, 2013;
Donat et al., 2013a; Donat et al., 2013c; van der Schrier et al., 2013) and selected regional studies as indicated. Bold text indicates where the assessment is somewhat different to
SREX Table 3-2. In each such case a footnote explains why the assessment is different. See also Figures 2.32 and 2.33.
Region
Warm Days
(e.g., TX90pa)
North America High confidence:
and Central
Likely overall increase
America
but spatially varying
trends1,2
Extreme Precipitation Dryness (e.g,. CDDa)
(e.g., RX1daya,
/ Droughth
a
a
R95p , R99p )
Cold Days
(e.g., TX10pa)
Warm Nights
(e.g., TN90pa, TRa)
Cold Nights / Frosts
(e.g., TN10pa, FDa)
Heat Waves / Warm
Spellsg
High confidence:
Likely overall
decrease but with
spatially varying
trends1,2
High confidence:
Likely overall
increase1,2
High confidence:
Likely overall
decrease1,2
Medium confidence: High confidence:
increases in more
Likely overall
regions than
increase1,2,5 but some
1,3
decreases but 1930s spatial variation
dominates longer term
High confidence:
trends in the USA4
Very likely increase
central North
America6,7
Medium confidence:
decrease1
but spatially varying
trends
High confidenceb:
Likely decrease
central North
America4
South America Medium confidenceb: Medium confidenceb: Medium confidenceb: Medium confidenceb: Low confidence:
Overall increase8
Overall decrease8
Overall increase8
Overall decrease8
insufficient evidence
(lack of literature)
and spatially varying
trends but some
evidence of increases
in more areas than
decreases8
Medium
confidenceb:
Increases in more
regions than
decreases8,9 but
spatially varying
trends
Low confidence:
Limited literature and
spatially varying
trends8
Europe and
High confidence:
Mediterranean Likely overall
increase10,11,12
High confidenceb,c:
Likely increases in
more regions than
decreases5,15,16 but
regional and
seasonal variation
Medium confidence:
spatially varying
trends
Africa and
Middle East
High confidence:
Likely overall
decrease11,12
High confidence:
Likely overall
increase11,12
High confidence:
Likely overall
decrease10,11,12
High confidenceb:
Likely increases in
most regions3,13
Low confidenced:
Low confidenced:
insufficient evidence insufficient evidence
(lack of literature)
and spatially varying
trends
Medium confidence:
increase in North
Medium
Africa and Middle
confidenceb:
b
b
b
b
Medium confidence : Medium confidence : Medium confidence : Medium confidence : East and southern
increases in more
Low to medium
confidenceb,d:
limited data in many
regions but increases
in most regions
assessed
Do Not Cite, Quote or Distribute
Low to medium
confidenceb,d:
limited data in many
regions but decreases
in most regions
assessed
Medium
confidenceb,d:
limited data in many
regions but increases
in most regions
assessed
Medium
confidenceb,d:
limited data in many
regions but decreases
in most regions
assessed
2-110
High confidenceb:
Likely increase in
Mediterranean17,18
Medium confidenced:
increase19,22,24
High confidenceb:
likely increase in
West Africa25,26
although 1970s Sahel
drought dominates
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increase North
Africa and Middle
East19,20
decrease North
Africa and Middle
East19,20
increase North
Africa and Middle
East19,20
decrease North
Africa and Middle
East19,20
IPCC WGI Fifth Assessment Report
Africa3,19,21,22
the trend
regions than
decreases in
southern Africa but
spatially varying
trends depending on
index5,21,22
Medium
confidenceb,e:
Spatially varying
trends and
insufficient data in
some regions
Low to medium
confidenceb,e:
High confidenceb:
High confidenceb:
High confidenceb:
High confidenceb:
Likely increase
Likely decrease
Likely increase
Likely decrease
southern Africa21,22,23 southern Africa21,22,23 southern Africa21,22,23 southern Africa21,22,23
Asia (excluding High confidenceb,e:
South-east
Likely overall
increase27,28,29,30,31,32
Asia)
High confidenceb,e:
Likely overall
decrease27,28,29,30,31,32
High confidenceb,e:
Likely overall
increase27,28,29,30,31,32
High confidenceb,e:
Likely overall
decrease27,28,29,30,31,32
Low to medium
confidenceb,e
Low confidence due Medium confidence:
to insufficient
Increase in eastern
evidence or spatially Asia36,37
varying trends.
High confidenceb,c:
Likely more areas of Medium confidence:
increases in more
increases than
decreases3,28,33
regions than
decreases5,34,35,36
South-east Asia High confidenceb,f:
and Oceania
Likely overall
increase27,38,39,40
High confidenceb,f:
Likely overall
decrease27,38,39
High confidenceb,f:
Likely overall
increase27,38,39,40
High confidenceb,f:
Likely overall
decrease27,38,39
Low confidence (due Low confidence
lack of literature) to (lack of literature) to
high confidenceb,f
high confidenceb,f
depending on region
High confidence:
Likely decrease in
High confidence2:
southern Australia42,43
Likely overall
but index and season
increase in
Australia3,14,41
dependent
Low to medium
confidenceb,f:
inconsistent trends
between studies in
SE Asia. Overall
increase in dryness
in southern and
eastern Australia
High confidenceb:
Likely increase
northwest
Australia25,26,44
1
2
3
4
5
6
7
8
(a) See Box 2.4, Table 1 for definitions
(b) More recent literature updates the assessment from SREX Table 3-2 (including ‘global’ studies).
(c) This represents a measure of the area affected which is different from what was assessed in SREX Table 3-2.
(d) This represents a slightly different region than that assessed in SREX Table 3-2 as it includes the Middle East.
(e) This represents a slightly different region than that assessed in SREX Table 3-2 as it excludes South-east Asia.
(f) This represents a slightly different region than that assessed in SREX Table 3-2 as it combines SE Asia and Oceania.
(g) Definitions for warm spells and heat waves vary (Perkins and Alexander, 2012) but here we are commonly assessing the Warm Spell Duration Index (WSDI; Zhang et al., 2011)
or other heat wave indices (e.g., HWF, HWM
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1
2
3
4
5
6
7
8
9
10
11
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IPCC WGI Fifth Assessment Report
(Fischer and Schar, 2010; Perkins et al., 2012) that have defined multi-day heat extremes relative to either daily maximum or minimum temperatures (or both) above a high
(commonly 90th) percentile relative to a late-20th century reference period.
(h) See Box 2.4 and Section 2.6.1 for definitions
(1)
Kunkel et al. (2008), 2 Peterson et al. (2008), 3 Perkins et al. (2012), 4 Peterson et al. (2013), 5 Westra et al. (2013), 6 Groisman et al. (2012), 7 Villarini et al. (2013), 8 Skansi et al.
(2013), 9 Haylock et al. (2006), 10 Andrade et al. (2012), 11 Efthymiadis et al. (2011), 12 Moberg et al. (2006), 13 Della-Marta et al. (2007a), 14 Perkins and Alexander (2012), 15 Van
den Besselaar et al. (2012), 16 Zolina et al. (2009), 17 Sousa et al. (2011), 18 Hoerling et al. (2012), 19 Donat et al. (2013b), 20 Zhang et al. (2005), 21 Kruger and Sekele (2013), 22 New
et al. (2006), 23 Vincent et al. (2011), 24 Aguilar et al. (2009), 25 Dai (2013), 26 Sheffield et al. (2012), 27 Choi et al. (2009), 28 Rahimzadeh et al. (2009), 29 Revadekar et al. (2012), 30
Tank et al. (2006), 31 You et al. (2010), 32 Zhou and Ren (2011), 33 Ding et al. (2010), 34 Krishna Moorthy et al. (2009), 35 Pattanaik and Rajeevan (2010), 36 Wang et al. (2012b), 37
Fischer et al. (2011), 38 Caesar et al. (2011), 39 Chambers and Griffiths (2008), 40 Wang et al. (2013), 41 Tryhorn and Risbey (2006), 42 Gallant et al. (2007), 43 King et al. (2013), 44
Jones et al. (2009)
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2
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Chapter 2
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Box 2.5, Table 1: Established indices of climate variability with global or regional influence. Z500, Z700, and Z850 denote geopotential height at the 500, 700 and 850 hPa levels,
respectively. Subscript s and a denote "standardized" and "anomalies", respectively. Further information is given in Supplementary Material 2.SM.8. Climate impacts of these modes
are listed in Box 14.1.
Climate Phenomenon
El Niño –
Traditional indices of
Southern
ENSO-related
Oscillation
Tropical Pacific
(ENSO)
climate variability
Index Name
NINO1+2
NINO3
NINO4
NINO3.4
Troup Southern Oscillation
Index (SOI)
SOI
Index Definition
SSTa averaged over [10°S–0°, 90°W–80°W]
Same as above but for [5°S–5°N, 150°W–90°W]
Same as above but for [5°S–5°N, 160°E–150°W]
Same as above but for [5°S–5°N, 170°W–120°W]
Standardized for each calendar month SLPa difference: Tahiti minus
Darwin, x10
Standardized difference of SLPsa: Tahiti minus Darwin
Darwin SOI
Equatorial SOI (EQSOI)
Darwin SLPsa
Standardized difference of standardized averages of SLPa over equatorial
[5°S–5°N] Pacific Ocean areas: [130°W–80°W] minus [90°E–140°E]
Indices of ENSO
Trans-Niño Index (TNI)
NINO1+2s minus NINO4s
events evolution and El Niño Modoki Index
SSTa [165°E–140°W, 10°S–10°N] minus ½*[110°W–70°W, 15°S–5°N]
type
(EMI)
minus ½*[125°E–145°E, 10°S–20°N]
Pacific Decadal and Interdecadal
Pacific Decadal Oscillation 1st PC of monthly N. Pacific SSTa field [20°N–70°N] with subtracted
Variability
(PDO)
global mean
Intedecadal Pacific
Projection of a global SSTa onto the IPO pattern, which is found as one of
Oscillation (IPO)
the leading Empirical Orthogonal Functions of a low-pass filtered global
SSTa field
North Pacific Index (NPI)
SLPa averaged over [30°N–65°N; 160°E–140°W]
North Atlantic Oscillation (NAO)
Azores-Iceland NAO Index SLPsa difference: Lisbon/Ponta Delgada minus Stykkisholmur/ Reykjavik
PC-based NAO Index
Leading PC of SLPa over the Atlantic sector
Gibraltar – South-west
Standardized for each calendar month SLPa difference: Gibraltar minus
Iceland NAO Index
SW Iceland / Reykjavik
Annular modes Northern Annular
PC-based NAM or Arctic
1st PC of the monthly mean SLPa poleward of 20°N
Mode (NAM)
Oscillation (AO) index
Southern Annular
PC-based SAM or Antarctic 1st PC of Z850a or Z700a south of 20°S
Mode (SAM)
Oscillation (AAO) index
Grid-based SAM index:
Difference between standardized zonally averaged SLPa at 40°S and 70°S,
40°S–70°S difference
using gridded SLP fields
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2-113
Primary Reference(s)
Rasmusson and Wallace (1983),
Cane (1986)
Trenberth (1997)
Troup (1965)
Trenberth (1984); Ropelewski and
Jones (1987)
Trenberth and Hoar (1996)
Bell and Halpert (1998)
Trenberth and Stepaniak (2001)
Ashok et al. (2007)
Mantua et al. (1997); Zhang et al.
(1997)
Folland et al. (1999); Power et al.
(1999); Parker et al. (2007)
Trenberth and Hurrell (1994)
Hurrell (1995)
Hurrell (1995)
Jones et al. (1997)
Thompson and Wallace (1998,
2000)
Thompson and Wallace (2000)
Nan and Li (2003)
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Final Draft
Pacific/North America (PNA)
atmospheric teleconnection
Pacific/South America (PSA)
atmospheric teleconnection
Chapter 2
Station-based SAM index:
40°S–65°S
PNA index based on centers
of action
PNA from rotated PCA
PSA1 and PSA2 mode
indices (PC-based)
PSA index (centers of
action)
Atlantic Ocean Multidecadal Variability Atlantic Multidecadal
Oscillation (AMO) index
Revised AMO index
Tropical Atlantic Atlantic Ocean Niño ATL3
Ocean
Mode (AONM)
PC-based AONM
variability
Tropical Atlantic
PC-based AMM Index
Meridional Mode
(AMM)
Tropical Indian Indian Ocean Basin Basin mean index (BMI)
Ocean
Mode (IOBM)
IOBM, PC-based Index
variability
Indian Ocean Dipole PC-based IODM index
Mode (IODM)
Dipole Mode Index (DMI)
IPCC WGI Fifth Assessment Report
Difference in standardized zonal mean SLPa at 40°S and 65°S, using
station data
¼[(20°N, 160°W) - (45°N, 165°W) + (55°N, 115°W) - (30°N, 85°W)] in
the Z500sa field
Rotated PC (RPC) from the analysis of the NH Z500a field
2nd and 3rd PCs respectively of SH seasonal Z500a
Marshall (2003)
[-(35°S, 150°W) + (60°S, 120°W) - (45°S, 60°W)] in the Z500a field
[(45°S, 170°W) - (67.5°S, 120°W) + (50°S, 45°W)]/3 in the Z500a field
10-yr running mean of linearly detrended Atlantic mean SSTa [0°–70°N]
Karoly (1989)
Yuan and Li (2008)
Enfield et al. (2001)
As above, but detrended by subtracting SSTa [60°S–60°N] mean
SSTa averaged over [3°S–3°N, 20°W–0°]
1st PC of the detrended tropical Atlantic monthly SSTa (20°S–20°N)
2nd PC of the detrended tropical Atlantic monthly SSTa (20°S–20°N)
Trenberth and Shea (2006)
Zebiak (1993)
Deser et al. (2010b)
Wallace and Gutzler (1981)
Barnston and Livezey (1987).
Mo and Paegle (2001)
SSTa averaged over [40°–110°E, 20°S–20°N]
Yang et al. (2007)
The 1st PC of the IO detrended SSTa (40°E–110° E, 20°S–20°N)
Deser et al. (2010b)
The 2nd PC of the IO detrended SSTa (40°E–110° E, 20°S–20°N)
SSTa difference: [50°E–70°E, 10°S–10°N] minus [90°E–110°E, 10°S–0°] Saji et al. (1999)
1
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Chapter 2
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Chapter 2: Observations: Atmosphere and Surface
Coordinating Lead Authors: Dennis L. Hartmann (USA), Albert M.G. Klein Tank (Netherlands), Matilde
Rusticucci (Argentina)
Lead Authors: Lisa Alexander (Australia), Stefan Broennimann (Switzerland), Yassine Abdul-Rahman
Charabi (Oman), Frank Dentener (EU/Netherlands), Ed Dlugokencky (USA), David Easterling (USA),
Alexey Kaplan (USA), Brian Soden (USA), Peter Thorne (USA/Norway/UK), Martin Wild (Switzerland),
Panmao Zhai (China)
Contributing Authors: Robert Adler (USA), Richard Allan (UK), Robert Allan (UK), Donald Blake
(USA), Owen Cooper (USA), Aiguo Dai (USA), Robert Davis (USA), Sean Davis (USA), Markus Donat
(Australia), Vitali Fioletov (Canada), Erich Fischer (Switzerland), Leopold Haimberger (Austria), Ben Ho
(USA), John Kennedy (UK), Elizabeth Kent (UK), Stefan Kinne (Germany), James Kossin (USA), Norman
Loeb (USA), Cathrine Lund Myhre (Norway), Carl Mears (USA), Christopher Merchant (UK), Steve
Montzka (USA), Colin Morice (UK), Joel Norris (USA), David Parker (UK), Bill Randel (USA), Andreas
Richter (Germany), Matthew Rigby (UK), Ben Santer (USA), Dian Seidel (USA), Tom Smith (USA), David
Stephenson (UK), Ryan Teuling (Netherlands), Junhong Wang (USA), Xiaolan Wang (Canada), Ray Weiss
(USA), Kate Willett (UK), Simon Wood (UK)
Review Editors: Jim Hurrell (USA), Jose Marengo (Brazil), Fredolin Tangang (Malaysia), Pedro Viterbo
(Portugal)
Date of Draft: 7 June 2013
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Figures
Figure 2.1: a) Globally averaged CO2 dry-air mole fractions from Scripps Institution of Oceanography (SIO) at
monthly time resolution based on measurements from Mauna Loa, Hawaii and South Pole (red) and
NOAA/ESRL/GMD at quasi-weekly time resolution (blue). SIO values are deseasonalized. b) Instantaneous growth
rates for globally averaged atmospheric CO2 using the same colour code as in (a). Growth rates are calculated as the
time derivative of the deseasonalized global averages (Dlugokencky et al., 1994).
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Figure 2.2: a) Globally averaged CH4 dry-air mole fractions from UCI (green; 4 values per year, except prior to 1984,
when they are of lower and varying frequency), AGAGE (red; monthly), and NOAA/ESRL/GMD (blue; quasi-weekly).
b) Instantaneous growth rate for globally averaged atmospheric CH4 using the same colour code as in (a). Growth rates
were calculated as in Figure 2.1.
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Figure 2.3: a) Globally averaged N2O dry-air mole fractions from AGAGE (red) and NOAA/ESRL/GMD (blue) at
monthly resolution. b) Instantaneous growth rates for globally averaged atmospheric N2O. Growth rates were calculated
as in Figure 2.1.
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Figure 2.4: Globally averaged dry-air mole fractions at Earth’s surface of the major halogen-containing well-mixed
GHG. These are derived mainly using monthly mean measurements from the AGAGE and NOAA/ESRL/GMD
networks. For clarity, only the most abundant chemicals are shown in different compound classes and results from
different networks have been combined when both are available.
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Figure 2.5: Water vapour anomalies in the lower stratosphere (~16–19 km) from satellite sensors and in situ
measurements normalized to 2000–2011. (a) Monthly mean water vapour anomalies at 83 hPa for 60°S–60°N (blue)
determined from HALOE and MLS satellite sensors. (b) Approximately monthly balloon-borne measurements of
stratospheric water vapour from Boulder, Colorado at 40°N (green dots; green curve is 15-point running mean)
averaged over 16–18 km and monthly means as in (a), but averaged over 30°N–50°N (black).
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Figure 2.6: Zonally averaged, annual mean total column ozone in Dobson Units (DU; 1 DU = 2.69 × 1016 O3/cm2) from
ground-based measurements combining Brewer, Dobson, and filter spectrometer data WOUDC (red),
GOME/SCIAMACHY/GOME-2 GSG (green) and merged satellite BUV/TOMS/SBUV/OMI MOD V8 (blue) for a)
Non-Polar Global (60°S–60°N), b) NH (30°N–60°N), c) Tropics (25°S–25°N), d) SH (30°S–60°S) and e) March NH
Polar (60°N–90°N) and October SH Polar. Adapted from Weber et al. (2012; see also for abbreviations).
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O3 (ppb)
(a)
Jungfraujoch - 46ºN, 3.6 km
Zugspitze - 47ºN, 3.0 km
Arosa - 47ºN, 1.0 km
Hohenpeissenberg - 48ºN, 1.0 km
60
40
IPCC WGI Fifth Assessment Report
Europe
20
Mace Head - 55ºN, 0.2 km
Arkona-Zingst - 54ºN, 0.0 km
0
O3 (ppb)
40
(b)
Mt. Happo - 36ºN, 1.9 km
Japanese MBL - 38-45ºN, ≤0.1 km
60
Asia
North America
20
Lassen NP - 41ºN, 1.8 km
U.S. Pacific MBL - 38-48ºN, ≤0.2 km
0
Summit, Greenland - 72.6ºN, 3.2 km
Barrow, Alaska - 71.3ºN, 0.0 km
Storhofdi, Iceland - 63.3ºN, 1.0 km
Mauna Loa, Hawaii - 19.5ºN, 3.4 km
Samoa - 14.2ºS, 0.1 km
Cape Point, South Africa - 34.4ºS, 0.2 km
Cape Grim, Tasmania - 40.7ºS, 0.1 km
O3 (ppb)
60
(c)
40
Other
latitudes
20
Arrival Heights, Antarctica - 77.8ºS, 0.1 km
South Pole- 90.0ºS, 2.8 km
0
1950
1960
1970
1980
1990
2000
2010
Figure 2.7: Annual average surface ozone concentrations from regionally representative ozone monitoring sites around
the world. a) Europe. b) Asia and North America. c) Remote sites in the Northern and Southern Hemispheres. The
station name in the legend is followed by its latitude and height. Time series include data from all times of day and
trend lines are linear regressions following the method of Parrish et al. (2012). Trend lines are fit through the full time
series at each location, except for Jungfraujoch, Zugspitze, Arosa and Hohenpeissenberg where the linear trends end in
2000 (indicated by the dashed vertical line in a)). Twelve of these 19 sites have significant positive ozone trends (i.e., a
trend of zero lies outside the 90% confidence interval); the 7 sites with non-significant trends are: Japanese MBL
(marine boundary layer), Summit (Greenland), Barrow (Alaska), Storhofdi (Iceland), Samoa (tropical South Pacific
Ocean), Cape Point (South Africa) and South Pole (Antarctica).
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Figure 2.8: Relative changes in tropospheric NO2 column amounts (logarithmic scale) in 7 selected world regions
dominated by high NOx emissions. Values are normalized for 1996 and derived from the GOME (Global Ozone
Monitoring Experiment) instrument from 1996 to 2002 and SCIAMACHY (Scanning Imaging Spectrometer for
Atmospheric Cartography) from 2003 to 2010 (Hilboll et al., 2013). The regions are indicated in the map inset.
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Figure 2.9: a) Annual average Aerosol Optical Depth (AOD) trends at 0.55 μm for 2000–2009, based on deseasonalized, conservatively cloud-screened MODIS aerosol data over oceans (Zhang and Reid, 2010). Negative AOD
trends off Mexico are due to enhanced volcanic activity at the beginning of the record. Most non-zero trends are
significant (i.e., a trend of zero lies outside the 95% confidence interval). b) Seasonal average AOD trends at 0.55 μm
for 1998 to 2010 using SeaWiFS data (Hsu et al., 2012). Grey areas indicate incomplete or missing data. Black dots
indicate significant trends (i.e., a trend of zero lies outside the 95% confidence interval).
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Figure 2.10: Trends in particulate matter (PM10 and PM2.5 with aerodynamic diameters < 10 and 2.5 μm, respectively)
and sulphate in Europe and USA for two overlapping periods 2000-2009 (a, b, c) and 1990-2009 (d, e). The trends are
based on measurements from the EMEP (Torseth et al., 2012) and IMPROVE (Hand et al., 2011) networks in Europe
and USA, respectively. Sites with significant trends (i.e., a trend of zero lies outside the 95% confidence interval) are
shown in colour; black dots indicate sites with non-significant trends.
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Box 2.2, Figure 1: a) Global Mean Surface Temperature (GMST) anomalies relative to a 1961–1990 climatology based
on HadCRUT4 annual data. The straight black lines are Least Squares trends for 1901–2012, 1901–1950 and 1951–
2012. b) Same data as in a), with Smoothing Spline (solid curve) and the 90% confidence interval on the smooth curve
(dashed lines). Note that the (strongly overlapping) 90% confidence intervals for the least square lines in (a) are omitted
for clarity. See Figure 2.20 for the other two GMST data products.
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Figure 2.11: Global mean energy budget under present day climate conditions. Numbers state magnitudes of the
individual energy fluxes in W/m2, adjusted within their uncertainty ranges to close the energy budgets. Numbers in
parentheses attached to the energy fluxes cover the range of values in line with observational constraints. Figure
adapted from Wild et al. (2013).
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Figure 2.12: Comparison of net Top Of Atmosphere (TOA) flux and upper Ocean Heating Rates (OHR). Global annual
average (July to June) net TOA flux from CERES observations (based upon the EBAF-TOA_Ed2.6r product)(black
line) and 0-700 (blue) and 0-1,800 m (red) OHR from the Pacific Marine Environmental Laboratory/Jet Propulsion
Laboratory/Joint Institute for Marine and Atmospheric Research (PMEL/JPL/JIMAR), 0-700 m OHR from the National
Oceanic Data Center (NODC) (Levitus et al., 2009)(green), and 0-700 m OHR from the Hadley Center (Palmer et al.,
2007)(brown). The length of the coloured bars corresponds to the magnitude of OHR. Thin vertical lines are error bars,
corresponding to the magnitude of uncertainties. Uncertainties for all annual OHR are given at one standard error
derived from ocean heat content anomaly uncertainties (Lyman et al., 2010). CERES net TOA flux uncertainties are
given at the 90% confidence level determined following Loeb et al. (2012). Adapted from Loeb et al.(2012).
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Figure 2.13: Annual mean Surface Solar Radiation (SSR) as observed at Stockholm, Sweden, from 1923 to 2010.
Stockholm has the longest SSR record available worldwide. Updated from Wild (2009) and Ohmura (2009).
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Figure 2.14: Global annual average Land-Surface Air Temperature (LSAT) anomalies relative to a 1961–1990
climatology from the latest versions of four different datasets (Berkeley, CRUTEM, GHCN and GISS).
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Figure 2.15: Temporal changes in the prevalence of different measurement methods in the International
Comprehensive Ocean-Atmosphere Dataset (ICOADS). a) fractional contributions of observations made by different
measurement methods: bucket observations (blue), engine room intake (ERI) and hull contact sensor observations
(green), moored and drifting buoys (red), and unknown (yellow). b) Global annual average Sea Surface Temperature
(SST) anomalies based on different kinds of data: ERI and hull contact sensor (green), bucket (blue), buoy (red), and all
(black). Averages are computed over all 5°  5° where both ERI/hull and bucket measurements, but not necessarily
buoy data, were available. Adapted from Kennedy et al. (2011a).
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Figure 2.16: Global annual average Sea Surface Temperature (SST) and Night Marine Air Temperature (NMAT)
relative to a 1961–1990 climatology from gridded datasets of SST observations (HadSST2 and its successor HadSST3),
the raw SST measurement archive (ICOADS, v2.5) and night marine air temperatures dataset HadNMAT2 (Kent et al.,
2013). HadSST2 and HadSST3 both are based on SST observations from versions of the ICOADS dataset, but differ in
degree of measurement bias correction.
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Figure 2.17: Global monthly mean Sea Surface Temperature (SST) anomalies relative to a 1961–1990 climatology
from satellites (ATSRs) and in situ records (HadSST3). Black lines: the 100-member HadSST3 ensemble. Red lines:
ATSR-based night-time subsurface temperature at 0.2m depth (SST0.2m) estimates from the ATSR Reprocessing for
Climate (ARC) project. Retrievals based on three spectral channels (D3, solid line) are more accurate than retrievals
based on only two (D2, dotted line). Contributions of the three different ATSR missions to the curve shown are
indicated at the bottom. The in situ and satellite records were co-located within 5° × 5° monthly grid boxes: only those
where both datasets had data for the same month were used in the comparison. Adapted from Merchant et al. (2012).
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Figure 2.18: Global annual average Sea Surface Temperature (SST) and Night Marine Air Temperature (NMAT)
relative to a 1961–1990 climatology from state of the art datasets. Spatially interpolated products are shown by solid
lines; non-interpolated products by dashed lines.
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Figure 2.19: Decadal Global Mean Surface Temperature (GMST) anomalies (white vertical lines in grey blocks) and
their uncertainties (90% confidence intervals as grey blocks) based upon the Land-Surface Air Temperature (LSAT)
and Sea Surface Temperature (SST) combined HadCRUT4 (v4.1.1.0) ensemble (Morice et al., 2012). Anomalies are
relative to a 1961–1990 climatology.1850s indicates the period 1850-1859, and so on. NCDC MLOST and GISS
dataset best-estimates are also shown.
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Figure 2.20: Annual Global Mean Surface Temperature (GMST) anomalies relative to a 1961–1990 climatology from
the latest version of the three combined Land-Surface Air Temperature (LSAT) and Sea Surface Temperature (SST)
datasets (HadCRUT4, GISS and NCDC MLOST). Published dataset uncertainties are not included for reasons
discussed in Box 2.1.
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Figure 2.21: Trends in Global Mean Surface Temperature (GMST) from the three datasets of Figure 2.20 for 1901–
2012. White areas indicate incomplete or missing data. Trends have been calculated only for those grid boxes with
greater than 70% complete records and more than 20% data availability in first and last decile of the period. Black plus
signs (+) indicate grid boxes where trends are significant (i.e., a trend of zero lies outside the 90% confidence interval).
Differences in coverage primarily reflect the degree of interpolation to account for data void regions undertaken by the
dataset providers ranging from none beyond grid box averaging (HadCRUT4) to substantial (GISS).
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Figure 2.22: Trends in Global Mean Surface Temperature (GMST) from NCDC MLOST for three non-consectutive
shorter periods (1911–1940; 1951–1980; 1981–2012). White areas indicate incomplete or missing data. Trends and
significance have been calculated as in Figure 2.22.
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Figure 2.23: Vertical weighting functions for those satellite temperature retrievals discussed in this chapter (modified
from Seidel et al. (2011)). The dashed line indicates the typical maximum altitude achieved in the historical radiosonde
record. The three SSU channels are denoted by the designated names 25, 26 and 27. LS (Lower Stratosphere) and MT
(Mid Troposphere) are two direct MSU measures and LT (Lower Troposphere) and *G (Global Troposphere) are
derived quantities from one or more of these that attempt to remove the stratospheric component from MT.
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Figure 2.24: Global annual average lower stratospheric (top) and lower tropospheric (bottom) temperature anomalies
relative to a 1981–2010 climatology from different datasets. STAR does not produce a lower tropospheric temperature
product. Note that the y-axis resolution differs between the two panels.
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Figure 2.25: Trends in MSU upper air temperature over 1979–2012 from UAH (left hand panels) and RSS (right hand
panels) and for LS (top row) and LT (bottom row). Data are temporally complete within the sampled domains for each
dataset. White areas indicate incomplete or missing data. Black plus signs (+) indicate grid boxes where trends are
significant (i.e., a trend of zero lies outside the 90% confidence interval).
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Figure 2.26: Trends in upper air temperature for all available radiosonde data products that contain records for 1958–
2012 for the globe (top) and tropics (20°N–20°S) and extra-tropics (bottom). The bottom panel trace in each case is for
trends on distinct pressure levels. Note that the pressure axis is not linear. The top panel points show MSU layer
equivalent measure trends. MSU layer equivalents have been processed using the method of Thorne et al. (2005). No
attempts have been made to sub-sample to a common data mask.
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Figure 2.27: As Figure 2.26 except for the satellite era 1979–2012 period and including MSU products (RSS, STAR
and UAH).
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Glacier Volume
Air Temperature
in the lowest few Km (troposphere)
Water Vapor
Temperature
Over Land
Sea Ice Area
Snow Cover
Marine Air Temperature
Sea Surface Temperature
Sea Level
Ocean Heat Content
FAQ 2.1, Figure 1: Independent analyses of many components of the climate system that would be expected to change
in a warming world exhibit trends consistent with warming (arrow direction denotes the sign of the change), as shown
in FAQ 2.1, Figure 2.
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0.6 Tropospheric temperature:
0.4 7 datasets
0.2
0.0
-0.2
-0.4
-0.6
-0.8
20
Ocean heat content(0-700m):
5 datasets
10
Land surface air temperature: 4 datasets
0.5
0.0
-0.5
-1.0
Temperature
anomaly (ºC)
0.4
Sea-surface temperature: 5 datasets
0.2
0.0
0
-0.2
-0.4
-10
-0.6
0.2
Sea level
anomaly (mm)
Specific humidity:
4 datasets
0.2
0.0
0.0
-0.2
-0.4
-0.6
100
Extent (106km2)
0.4
Marine air temperature: 2 datasets
Specific humidity
anomaly (g/kg)
Temperature
anomaly (ºC)
0.4
-0.2
Sea level: 6 datasets
6 Northern hemisphere (March4 April) snow cover: 2 datasets
50
0
2
-50
0
-100
-2
-150
-4
-200
12
-6
10
5
10
Summer arctic sea-ice extent: 6 datasets
Glacier mass balance:
3 datasets
0
8
-5
6
-10
4
1850
1900
1950
2000
Year
-15
1940
1960
1980
Temperature
anomaly (ºC)
IPCC WGI Fifth Assessment Report
Ocean heat content
anomaly (1022 J)
Temperature
anomaly (ºC)
1.0
Chapter 2
Mass balance (1015GT) Extent anomaly (106km2)
Final Draft (7 June 2013)
2000
Year
FAQ 2.1, Figure 2: Multiple independent indicators of a changing global climate. Each line represents an
independently-derived estimate of change in the climate element. In each panel all datasets have been normalized to a
common period of record. A full detailing of which source datasets go into which panel is given in the Supplementary
Material 2.SM.5.
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Figure 2.28: Annual precipitation anomalies averaged over land areas for four latitudinal bands and the globe from five
global precipitation datasets relative to a 1981–2000 climatology.
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Figure 2.29: Trends in precipitation over land from the CRU, GHCN and GPCC datasets for 1901–2010 (left hand
panels) and 1979–2010 (right hand panels). Trends have been calculated only for those grid boxes with greater than
70% complete records and more than 20% data availability in first and last decile of the period. White areas indicate
incomplete or missing data. Black plus signs (+) indicate grid boxes where trends are significant (i.e., a trend of zero
lies outside the 90% confidence interval).
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Figure 2.30: a) Trends in surface specific humidity from HadISDH and NOCS over 1973–2012. Trends have been
calculated only for those grid boxes with greater than 70% complete records and more than 20% data availability in first
and last decile of the period. White areas indicate incomplete or missing data. Black plus signs (+) indicate grid boxes
where trends are significant (i.e., a trend of zero lies outside the 90% confidence interval). b) Global annual average
anomalies in land surface specific humidity from Dai (2006; red), HadCRUH (Willett et al., 2013; orange), HadISDH
(Willett et al., 2013; black), and ERA-Interim (Simmons et al., 2010; blue). Anomalies are relative to the 1979-2003
climatology.
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Figure 2.31: a) Trends in column integrated water vapour over ocean surfaces from Special Sensor Microwave Imager,
(Wentz et al., 2007) for the period 1988–2010. Trends have been calculated only for those grid boxes with greater than
70% complete records and more than 20% data availability in first and last decile of the period. Black plus signs (+)
indicate grid boxes where trends are significant (i.e., a trend of zero lies outside the 90% confidence interval). b) Global
annual average anomalies in column integrated water vapour averaged over ocean surfaces. Anomalies are relative to
the 1988–2007 average.
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Figure 2.32: Trends in annual frequency of extreme temperatures over the period 1951–2010, for: (a) cold nights
(TN10p), (b) cold days (TX10p), (c) warm nights (TN90p) and (d) warm days (TX90p) (Box 2.4, Table 1). Trends were
calculated only for grid boxes that had at least 40 years of data during this period and where data ended no earlier than
2003. Grey areas indicate incomplete or missing data. Black plus signs (+) indicate grid boxes where trends are
significant (i.e., a trend of zero lies outside the 90% confidence interval). The data source for trend maps is HadEX2
(Donat et al., 2013c) updated to include the latest version of the European Climate Assessment dataset (Klok and Tank,
2009). Beside each map are the near-global time series of annual anomalies of these indices with respect to 1961–1990
for three global indices datasets: HadEX2 (red); HadGHCND (Caesar et al., 2006; blue) and updated to 2010 and
GHCNDEX (Donat et al., 2013a; green). Global averages are only calculated using grid boxes where all three datasets
have at least 90% of data over the time period. Trends are significant (i.e., a trend of zero lies outside the 90%
confidence interval) for all the global indices shown.
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Figure 2.33: Trends in (a) annual amount of precipitation from days >95th percentile (R95p) (b) daily precipitation
intensity (SDII) and (c) frequency of the annual maximum number of consecutive dry days (CDD) (Box 2.4, Table 1).
Trends are shown as relative values for better comparison across different climatic regions. Trends were calculated only
for grid boxes that had at least 40 years of data during this period and where data ended no earlier than 2003. Grey areas
indicate incomplete or missing data. Black plus signs (+) indicate grid boxes where trends are significant (i.e., a trend of
zero lies outside the 90% confidence interval). The data source for trend maps is HadEX2 (Donat et al., 2013a) updated
to include the latest version of the European Climate Assessment dataset (Klok and Tank, 2009). (d) Trends
(normalized units) in hydroclimatic intensity (HY-INT: a multiplicative measure of length of dry spell and precipitation
intensity) over the period 1976–2000 (adapted from Giorgi et al. (2011). An increase (decrease) in HY-INT reflects an
increase (decrease) in the length of drought and /or extreme precipitation events.
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Normalized units
(a)
Normalized units
Chapter 2
(b)
Normalized units
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(c)
IPCC WGI Fifth Assessment Report
Landfalling Tropical Cyclones, Eastern Australia
Landfalling Hurricanes, United States
Landfalling Typhoons, China
1900
1950
2000
Figure 2.34: Normalized 5-year running means of the number of (a) adjusted land falling eastern Australian tropical
cyclones (adapted from Callaghan and Power (2011) and updated to include 2010/2011 season) and (b) unadjusted land
falling U.S. hurricanes (adapted from Vecchi and Knutson (2011) and (c) land-falling typhoons in China (adapted from
CMA, 2011). Vertical axis ticks represent one standard deviation, with all series normalized to unit standard deviation
after a 5-year running mean was applied.
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Figure 2.35: 99th percentiles of geostrophic wind speeds for winter (DJF). Triangles show regions where geostrophic
wind speeds have been calculated from in situ surface pressure observations. Within each pressure triangle, Gaussian
low-pass filtered curves and estimated linear trends of the 99th percentile of these geostrophic wind speeds for winter
are shown. The ticks of the time (horizontal) axis range from 1875 to 2005, with an interval of 10 years. Disconnections
in lines show periods of missing data. Red (blue) trend lines indicate upward (downward) significant trends (i.e., a trend
of zero lies outside the 95% confidence interval). From Wang et al. (2011).
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Box 2.4, Figure 1: Trends in the warmest day of the year using different datasets for the period 1951–2010. The
datasets are (a) HadEX2 (Donat et al., 2013c) updated to include the latest version of the European Climate Assessment
dataset (Klok and Tank, 2009), (b) HadGHCND (Caesar et al., 2006) using data updated to 2010 (Donat et al., 2013a),
and (c) Globally averaged annual warmest day anomalies for each dataset. Trends were calculated only for grid boxes
that had at least 40 years of data during this period and where data ended no earlier than 2003. Grey areas indicate
incomplete or missing data. Black plus signs (+) indicate grid boxes where trends are significant (i.e., a trend of zero
lies outside the 90% confidence interval). Anomalies are only calculated using grid boxes where both datasets have data
and where 90% of data are available.
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0.08
IPCC WGI Fifth Assessment Report
(a) Daily Minimum
Temperatures
Probability
0.06
0.04
0.02
0.08
(b) Daily Maximum
Temperatures
Probability
0.06
0.04
0.02
-15
-10
-5
0
10
Temperature Anomaly (ºC)
5
15
FAQ 2.2, Figure 1: Distribution of (a) daily minimum and (b) daily maximum temperature anomalies relative to a
1961–1990 climatology for two periods: 1951–1980 (blue) and 1981–2010 (red) using the HadGHCND dataset. The
shaded blue and red areas represent the coldest 10% and warmest 10% respectively of (a) nights and (b) days during the
1951–1980 period. The darker shading indicates by how much the number of the coldest days and nights has reduced
(dark blue) and by how much the number of the warmest days and nights has increased (dark red) during the 1981–2010
period compared to the 1951–1980 period.
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Heavy Precipitation Events
Droughts Mediterranean,
West Africa
Droughts Central North America
Northwest Australia
Hot Days and Nights;
Warm Spells and Heat Waves
Cold Days and Nights
Strongest Tropical Cyclones North Atlantic
FAQ 2.2, Figure 2: Trends in the frequency (or intensity) of various climate extremes (arrow direction denotes the sign
of the change) since the middle of the 20th century (except for North Atlantic storms where the period covered is from
the 1970s).
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Figure 2.36: Trends in (left) Sea Level Pressure (SLP), (middle) 500 hPa geopotential height (GPH), and (right) 100
hPa GPH in (top) November to April 1979/1980 to 2011/2012 and (bottom) May to October 1979 to 2011 from ERAInterim data. Trends are only shown if significant (i.e., a trend of zero lies outside the 90% confidence interval).
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Figure 2.37: Decadal averages of Sea Level Pressure (SLP) from the 20th century Reanalysis (20CR) for (left)
November of previous year to April and (right) May to October shown by two selected contours: 1004 hPa (dashed
lines) and 1020.5 hPa (solid lines). Topography above 2 km above mean sea level in 20CR is shaded in dark grey.
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Figure 2.38: Trends in surface wind speed for 1988–2010. Shown in the top row are datasets based on the satellite
wind observations: (a) Cross-Calibrated Multi-Platform wind product (CCMP, Atlas et al., 2011); (b) wind speed from
the Objectively Analyzed Air-Sea Heat Fluxes dataset, release 3 (OAFlux); (c) Blended Sea Winds (BSW, Zhang et al.,
2006); in the middle row are datasets based on surface observations: (d) wind speed from the Surface Flux Dataset, v.2,
from NOC, Southampton, U.K. (Berry and Kent, 2009); (e) Wave- and Anemometer-based Sea Surface Wind
(WASWind, (Tokinaga and Xie, 2011a)); (f) Surface Winds on the Land (Vautard et al., 2010); and in the bottom row
are surface wind speeds from atmospheric reanalyses: (g) ERA-Interim; (h) NCEP-NCAR, v.1 (NNR); and (i) 20th
century Reanalysis (20CR, Compo et al., 2011). Wind speeds correspond to 10 m heights in all products. Land station
winds (panel f) are also for 10 m (but anemometer height is not always reported) except for the Australian data where
they correspond to 2 m height. To improve readability of plots, all datasets (including land station data) were averaged
to the 4° × 4° uniform longitude-latitude grid. Trends were computed for the annually averaged timeseries of 4° × 4°
cells. For all datasets except land station data, an annual mean was considered available only if monthly means for no
less than eight months were available in that calendar year. Trend values were computed only if no less than 17 years
had values and at least one year was available among the first and last three years of the period. White areas indicate
incomplete or missing data. Black plus signs (+) indicate grid boxes where trends are significant (i.e., a trend of zero
lies outside the 90% confidence interval).
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Figure 2.39: a) Indices of the strength of the northern Hadley circulation in December to March (Ψmax is the maximum
of the meridional mass stream function at 500 hPa between the equator and 40°N. b) Indices of the strength of the
Pacific Walker circulation in September to January (Δω is the difference in the vertical velocity between [10°S to 10°N,
180°W to 100°W] and [10°S to 10°N, 100°E to 150°E] as in Oort and Yienger (1996), Δc is the difference in cloud
cover between [6°N–12°S, 165°E–149°W] and [18°N–6°N, 165°E–149°W] as in Deser et al. (2010a), vE is the effective
wind index from SSM/I satellite data, updated from Sohn and Park (2010), u is the zonal wind at 10 m averaged in the
region [10°S–10°N, 160°E–160°W], ΔSLP is the SLP difference between [5°S–5°N, 160°W–80°W] and [5°S–5°N,
80°E–160°E] as in Vecchi et al. (2006)). Reanalysis datasets include 20CR, NCEP/NCAR, ERA-Interim, JRA-25,
MERRA, and CFSR, except for the zonal wind at 10 m (20CR, NCEP/NCAR, ERA-Interim), where available until
January 2013. ERA-40 and NCEP2 are not shown as they are outliers with respect to the strength trend of the northern
Hadley circulation (Mitas and Clement, 2005; Song and Zhang, 2007; Hu et al., 2011; Stachnik and Schumacher, 2011).
Observation datasets include HadSLP2 (Section 2.7.1), ICOADS (Section 2.7.2, only 1957–2009 data are shown) and
WASWIND (Section 2.7.2), reconstructions are from Broennimann et al. (2009). Where more than one time series was
available, anomalies from the 1980/1981 to 2009/2010 mean values of each series are shown.
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Figure 2.40: Annual average tropical belt width (left) and tropical edge latitudes in each hemisphere (right). The
tropopause (red), Hadley cell (blue), and jet stream (green) metrics are based on reanalyses (NCEP/NCAR, ERA-40,
JRA25, ERA-Interim, CFSR, and MERRA, see Box 2.3); outgoing longwave radiation (orange) and ozone (black)
metrics are based on satellite measurements. The ozone metric refers to equivalent latitude (Hudson et al., 2006;
Hudson, 2012). Adapted and updated from Seidel et al. (2008) using data presented in Davis and Rosenlof (2011) and
Hudson (2012). Where multiple datasets are available for a particular metric, all are shown as light solid lines, with
shading showing their range and a heavy solid line showing their median.
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(a)
−2
1940
1960
1980
−2
2000
1880
(d)
Indices of Pacific Decadal/Interdecadal Variability
PDO
(−1)*NPI, 24−mon
IPO, 11−yr LPF
r.m. (HadSLP2r)
0
−2
1880
4
2
1900
1920
1940
1960
1980
1880
(g)
1900
1920
1940
1960
1980
(f)
Indices (s.d.)
0
−1
−2
1920
1940
1960
1980
Atlantic Ocean Nino Mode indices (HadISST1)
ATL3 (detrended)
AONM PC
2
1
0
−1
1880
1900
1920
1940
1960
1980
1880
1900
1920
1940
1960
1980
1940
1960
1980
2000
stations
40S−65S
1900
1920
1940
1960
1980
2000
PSA1 mode indices (NNR), 24−mon r.m.
Karoly PSA
Yuan&Li (−1)*PSA
PSA1 PC
1
0
−1
1880
1900
1920
1940
1960
1980
2000
(j) Tropical Atlantic Meridional Mode index (HadISST1)
3
AMM PC
2
1
0
−1
1880
(l)
Indian Ocean Basin Mode indices (HadISST1)
BMI (detrended)
IOBM PC
0
1920
2
2000
2
−2
2000
−2
−2
2000
2
Indices (s.d.)
Indices (s.d.)
Indices (s.d.)
1900
AMM index (s.d.)
1880
(k)
1980
0
1880
PNA indices, DJFM means
RPC
Centers of action (NNR)
1900
Indices of SAM/AAO
Z850 PC
grid 40S−70S
(NNR)
(HadSLP2r)
2
(h)
1
−2
1960
0
−0.2
−0.4
2000
2
3
1940
0.2
1880
Indices of NAO and NAM/AO, DJFM means
NAO PC
NAM (HadSLP2r)
NAO stations
(Hurrell,1995)
−2
(i)
1920
AMO indices (HadISST1): annual & 10−yr r.m.
AMO
10−yr r.m.
Revised
10−yr r.m.
0.6
0.4
2000
0
−4
1900
o
2
(e)
Indices (s.d.)
1920
0
Indices ( C)
Indices (s.d.)
4
1900
Additional indices of ENSO (HadISST1)
NINO4
(−1)*TNI
EMI
2
Indices (s.d.)
0
1880
IPCC WGI Fifth Assessment Report
(b)
Traditional indices of ENSO
Troup (−1)*SOI
(−1)*EQSOI
NINO3.4
(HadISST1)
(HadSLP2r)
2
(c)
Indices (s.d.)
Chapter 2
Indices (s.d.)
o
NINO3.4 ( C), SOIs (s.d.)
Final Draft (7 June 2013)
1920
1940
1960
1980
2000
Indian Ocean Dipole Mode indices (HadISST1)
DMI (detrended)
IODM PC
1
0
−1
−2
2000
1900
1880
1900
1920
1940
1960
1980
2000
Box 2.5, Figure 1: Some indices of climate variability, as defined in Box 2.5, Table 1, plotted in the 1870–2012
interval. Where ‘HadISST1’, ‘HadSLP2r’, or ‘NNR’ are indicated, the indices were computed from the Sea Surface
Temperature (SST) or Sea Level Pressure (SLP) values of the former two datasets or from 500 or 850 hPa geopotential
height fields from the NNR. Dataset references given in the panel titles apply to all indices shown in that panel. Where
no dataset is specified, a publicly available version of an index from the authors of a primary reference given in Box
2.5, Table 1 was used. All indices were standardized with regard to 1971–2000 period except for NINO3.4 (centralized
for 1971–2000) and AMO indices (centralized for 1901–1970). Indices marked as “detrended” had their linear trend for
1870–2012 removed. All indices are shown as 12-month running means except when the temporal resolution is
explicitly indicated (e.g., ‘DJFM’ for December-to-March averages) or smoothing level (e.g., 11-year LPF for a lowpass filter with half-power at 11 years).
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2-162
Total pages: 163
Final Draft (7 June 2013)
Chapter 2
IPCC WGI Fifth Assessment Report
Box 2.5, Figure 2: Spatial patterns of climate modes listed in Box 2.5, Table 1. All patterns shown here are obtained by
regression of either Sea Surface Temperature (SST) or Sea Level Pressure (SLP) fields on the standardized index of a
climate mode. For each climate mode one of the specific indices shown in Box 2.5, Figure 1 was used, as identified in
the panel subtitles. SST and SLP fields are from HadISST1 and HadSLP2r datasets (interpolated gridded products
based on datasets of historical observations). Regressions were done on monthly means for all patterns except for NAO
and PNA, which were done with the DJFM means, and for PSA1 and PSA2, where seasonal means were used. Each
regression was done for the longest period within the 1870-2012 interval when the index was available. For each pattern
the time series was linearly de-trended over the entire regression interval. All patterns are shown by color plots, except
for PSA2, which is shown by white contours over the PSA1 color plot (contour steps are 0.5 hPa, zero contour is
skipped, negative values are indicated by dash).
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2-163
Total pages: 163